Research ArticleA High-Capacity Image Steganography Method Using ChaoticParticle Swarm Optimization

Aya Jaradat Eyad Taqieddin and Moad Mowafi

Department of Network Engineering and Security Jordan University of Science and Technology Irbid Jordan

Correspondence should be addressed to Eyad Taqieddin eyadtaqjustedujo

Received 20 November 2020 Revised 21 March 2021 Accepted 31 May 2021 Published 8 June 2021

Academic Editor Jinwei Wang

Copyright copy 2021 Aya Jaradat et al )is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Image steganography has been widely adopted to protect confidential data Researchers have been seeking to improve thesteganographic techniques in order to increase the embedding capacity while preserving the stego-image quality In this paper wepropose a steganography method using particle swarm optimization and chaos theory aiming at finding the best pixel locations inthe cover image to hide the secret data while maintaining the quality of the resultant stego-image To enhance the embeddingcapacity the host and secret images are divided into blocks and each block stores an appropriate amount of secret bits Ex-perimental results show that the proposed scheme outperforms existing methods in terms of the PSNR and SSIM imagequality metrics

1 Introduction

Information security has become a major topic due to itsimportant role in data communication It offers techniquesto secure the information by avoiding any illegal accesschange damage or disclosure of it [1] )e impact andimportance of information security in daily life applicationsare supported by two ways cryptography and steganography[2 3] where steganography maintains the data formatpreserved [4] on the other hand cryptography converts thedata into difficult to read codes [5]

Steganography is a Greek word that comes from theword Steganos it appeared around 2500 years ago [6] andhas been used to embed information in different media suchas manuscripts videos images and audio It provides a wayto communicate safely so that it does not attract suspicionand attention during data transmission It goes hand-in-hand with the accompanying progress in the area of digitalcommunications which is clearly evident in the growth ofdata and users

)e efficiency of any steganographic method greatlydepends on the selection of the embedding area in the covermedium intelligently and accurately [7] )is can affect theembedding capacity and the quality of the cover medium In

this work we propose a new method for image hiding toimprove the efficiency of embedding without compromisingthe quality of the resulting stego-image )e proposedmethod utilizes the particle swarm optimization and chaostheory to implement the embedding process

Particle swarm optimization (PSO) is an efficient andeasy-to-implement algorithm It is closely linked to artificiallife systems and works in a collective and cooperativemanner [8] )e PSO algorithm does not require an enor-mous number of control parameters and can be easily ac-complished through balanced computing as it is applied togathering of particles PSO focuses mainly on a number ofrandom particles and aims to find the optimal solutionthrough repetition [9] It has been used in various fieldsincluding steganography For example in [10 11] PSO wasused to find the optimal pixel in the cover image to embedthe secret data

PSO depends on finding the best position for an idealparticle location and the best global particle location Howeverone drawback to using it is that the particles become susceptibleto premature convergence which leads to trapping the swarmin an ideal local area and the inability to discover any new arealocated in the local optimal solution )us access to the globaloptimal solution becomes relatively weak

HindawiSecurity and Communication NetworksVolume 2021 Article ID 6679284 11 pageshttpsdoiorg10115520216679284

In this work our goal is to avoid this problem byemploying the chaos theory [12] which depends mainly onrandom movement to generate random sequences that helpin preventing any repetitions in the search area Chaostheory can improve the quality of search by determining thelocation more accurately and achieves a balance between theglobal and local position search capabilities

)e rest of this paper is organized as follows Section 2reviews the related work and Section 3 presents backgroundon PSO and chaotic map Section 4 describes the proposedmethod and Section 5 discusses the experimental setup andevaluation of the results Section 6 concludes the paper andprovides future work

2 Related Work

In [13] the authors proposed a technique that relies on theVernam Cipher algorithm and the false random key andadopts the least significant bit (LSB) steganography methodin order to hide the secret image bits into the cover image In[14] a new scheme based on pixel-value differencing andPSO was proposed to hide confidential information in thecover image

)e authors in [15] used the principle of pixel over-lapping to hide a large amount of information with highimage quality In [16] a quantum information hidingprotocol was suggested based on image expansion using thequantum log-polar image representation and the Groversearch algorithm to extract the precisely designed secretmessage and determine the correct quantitative image copyIn [17] the genetic algorithm (GA) was used with a linearconvergence generator to hide confidential data into thecover image by specifying the appropriate locations to in-clude at least two bits in each pixel )e proposed modelprovided high cloaking for the data but low embeddingcapacity due to the inclusion of only two bits

)e work in [18] provided a technique depending on GAand LSB substitution to increase the embedding capacity andreduce the distortion that may affect the resultant image

In [19] a technique using the Knight Tour algorithmwith LSB is proposed It aims to shorthand the image andstrengthen the security of the stego-image )e Chi-squarestatistical attack was applied to evaluate the security level ofthe proposed method )e authors in [20] relied on dif-ferencing and substitution mechanisms in the process ofconcealing high-capacity information First LSB substitu-tion is applied to the two lower significant bits then thequotient value differencing (QVD) is applied to theremaining six bits

An embedding method was introduced in [21] based onthe use of PSO for optimal selection of pixel and wavelettransformation with the aim of masking a secret soundsignal in the cover image In [22] the authors proposed ahybrid information masking scheme that consists of amixture of noise visibility function and an optimal codingscheme to achieve an increase in the payload capacityFurthermore a steganography procedure was proposed in[23] depending on the PSO algorithm with the aim ofachieving consistent compatibility with the size of the secret

data so as to accomplish a perfect stego-image qualitythrough the substitution matrix intended to transform thesecret data in the cover image

)e authors in [24] presented a technique that dependson GA to embed the secret data bits into the cover images Asimple LSB is applied first then GA is used to change thepixel values to make the stego-image safe against RS attacksIn [25] another method that depends on GA was presentedIt divides the cover images into parts then the best pixels arefound in each part to complete the embedding process

In this paper we study applying the PSO algorithm withchaotic map in steganography aiming at improving thequality of the stego-image as well as the embedding capacity

3 Background

31 Standard PSO Algorithm )e PSO algorithm aims towork in a collective and cooperative manner in a smart wayIt appeared in 1995 and witnessed a number of enhance-ments in many areas as it is closely linked to the evolu-tionary algorithms and artificial life [8] It has manyadvantages including the following (a) it does not require alarge number of control parameters so it is anything but notdifficult to execute (b) it can be accomplished easily throughbalanced computing as it is applied to gathering of particlesIt focuses mainly on a number of random particles and aimsto find the optimal solution through repetition [9 26]

)e PSO algorithm depends mainly on the population asit consists of a group of individuals or particles calledswarms and these particles move to the search area withmultiple dimensions to start searching for a solution similarto searching for food )e individuals depend in their di-rections on the sociopsychological trends of other particleswhich leads to a change in particle conditions as a result ofthe change in speed )ese changes lead to the movement ofthe particles toward the best position in the search areawhich is known as the best global position (gbest) and eachmovement of the particle through its favorable position isknown as the best particle position (pbest) )ese two ac-tivities direct the particles to move toward the ideal solution[11] )e new location xt+1

i and velocity vt+1i of particle i at

iteration t + 1 can be expressed as

vt+1i w times v

ti + c1 times r1 times pbestti minus x

ti1113872 1113873

+ c2 times r2 times gbestt minus xti1113872 1113873

(1)

xt+1i x

ti + v

t+1i (2)

where r1 and r2 are random numbers between 0 and 1 c1and c2 are acceleration factors and w is the inertia weightthat controls the velocity

32 Chaotic Maps A chaotic map function indicates a typeof development that shows chaotic and random behaviorthat is not predictable but it is characterized by a type ofregularity and ergodicity that works to prevent pointsrepetition from occurring in the search ranges caused by therandom sequence)e chaotic equation is also characterized

2 Security and Communication Networks

by its sensitivity to the initial values If a slight change occursit leads to major changes in the behavior of the systemLogistic map is one of the simplest forms of a chaotic systemand it has been used in many fields including steganography[27 28] In this work we use the logistic map to produceprimary particles distributed in a bound structure andrecreate a specific number of particles More insights on thelogistic map are given in the next section

4 Proposed Method

One of the disadvantages of the PSO algorithm is thepremature convergence toward the optimal local solution Inthis work we propose a chaotic PSO (CPSO) algorithm tomake the PSO algorithm progressively effective in thesteganography process by locating the best pixel locations inthe cover image to embed the secret image bits whilemaintaining the quality of the stego-image resulting fromthe embedding process

41 CPSO Algorithm In the proposed algorithm we utilizethe logistic map to generate the chaotic variables and mapthese variables to the search space )e logistic equation canbe expressed as

zi+1 μzi 1 minus zi( 1113857 (3)

where zi is a chaotic variable in the interval (0 1) and μ is aparameter in the interval [0 4] that controls the behavior of

the logistic map When μ is in the range [3 4] the systembehaves in a chaotic manner

For the particles speed we utilize a sigmoid function foraccurate positioning which can be expressed by the fol-lowing equation

s vt+1i1113872 1113873

1

1 + eminus vt+1

i

(4)

In the traditional PSO algorithm random numbers areused to initialize the population particles Since the initialvelocity and direction of each particle in the population areirregular and not constant this may lead to missing somespatial positions On the other hand using a chaotic se-quence instead of the random numbers for initialization isconsidered a robust method that increases the diversity ofthe particles and enhances the performance of the PSOalgorithm by preventing premature convergence In theproposed algorithm the logistic map is applied when theparticlersquos speed and position are initialized and instead ofusing the random numbers r1 and r2 we use two chaoticsequences that are created according to the followingequations

xt+1i 0 r(t + 1)lt s v

t+1i1113872 1113873

xt+1i 1 r(t + 1)ge s v

t+1i1113872 1113873

⎧⎪⎨

⎪⎩(5)

where r(t + 1) is a real number generated between [0 1]Accordingly the particle velocity is calculated as follows

vt+1i w times v

ti + c1 times Chaotic1() times pbestti minus x

ti1113872 1113873 + c2 times Chaotic2() times gbestt minus x

ti1113872 1113873 (6)

where Chaotic is a function based on the logistic map resultsin equation (3) Algorithm 1 shows the pseudocode of theproposed CPSO algorithm

42 ScanningOrder inCover Image In this work we use LSBsubstitution to hide a secret image into the cover image Todo so we model the steganography problem as a searchproblem in which the goal is to find the best pixel locationsin the cover image to hide the secret image with minimumdistortion )is involves scanning the pixels of the coverimage and determining the order and position of the pixelsfor embedding the secret image )ere are different ordersand positions that give a different stego-image quality [10]Figure 1 shows examples of different pixel scanning ordersfor an image with dimension 5times 5 In this work we use theCPSO algorithm to find the best order and position in thecover image that maximizes the quality of the stego-image

43 Particles Representation In the CPSO algorithm nineparticles are defined that correspond to the variables shownin Table 1 [10] )e direction of pixel scanning in the coverimage consists of 16 possible states X-offset and Y-offsetrepresent the starting point position Bit-planes specify the

LSBs in the cover image that are used for embedding asshown in Table 2 [10] SB-pole and SB-dire specify the poleand direction of secret bits respectively while BP-Direspecifies the direction of the LSB planes as shown in Table 3[10]

44 Data Embedding To embed the secret message in ourcase an image S(k times v) into the cover image C(m times n) thesecret image is first divided into four separate blocks andeach block is converted into binary according to the SB-poleand SB-dire particles )en the MSB of each pixel isextracted to produce a sequence of bits with length l forembedding )e cover image is then divided into fourseparate blocks each of length w Accordingly the number ofsecret bits that can be embedded in each block can becalculated as lw After that the CPSO algorithm is appliedto find the best position of the pixels in each block Withinthe swarm the number of pixels in each block that areneeded to embed the secret bits should be determined as thenumber of pixels varies depending on the number of secretbits to be embedded Finally the number of pixel bits in thecover image is compared with the number of secret bits Ifthe number of secret bits is less than the pixel bits the secretbits are embedded into the pixels to obtain the stego-image

Security and Communication Networks 3

Otherwise the embedding process cannot be performedFigure 2 illustrates the process of data embedding andextraction

A common quality metric to measure the differencebetween the cover image and the stego-image is the peaksignal to noise ratio (PSNR) )e higher the PSNR value theless difference between the cover image and stego-image Inthe proposed CPSO algorithm the objective is to maximizethe value of PSNR In Algorithm 1 we consider the PSNRvalue as the objective function

f xi( 1113857 PSNR(C S) (7)

Accordingly the pbest and gbest values are iterativelyupdated based on the PSNR value pbest is updated when thePSNR value is greater than the previous one while gbest isupdated when the PSNR value is globally higher Algo-rithm 2 shows the pseudocode of the data embeddingprocess

45 Data Extraction To extract the secret image the par-ticles are first extracted from the last row of the stego-image)e sequence of secret bits is then extracted using thecorresponding particles to obtain the secret image Algo-rithm 3 shows the pseudocode of the data extraction process

Input Number of particles (N) maximum iterationsOutput Global best particles position (gbest)(1) for i 1 to N Initialize particles velocity and position(2) xi⟵Chaotic(xmin xmax)

(3) vi⟵Chaotic(vmin vmax)

(4) pbest⟵xi Initialize particles local best position(5) gbest⟵ ϕ Initialize particles global best position(6) end for(7) do(8) for i 1 to N

(9) if f(xi)gtf(pbesti)(10) pbesti⟵xi Update local best position(11) end if(12) if f(pbesti)gtf(gbest)(13) gbest⟵ pbesti Update global best position(14) end if(15) end for(16) for i 1 to N

(17) Update vi using equation (6)(18) Update xi using equation (2)(19) end for(20) while (Maximum iterations or stopping criterion is not attained)

ALGORITHM 1 Pseudocode of the proposed CPSO algorithm

1 2 3 4 5

6 7 8 9 10

11 12 13 14 15

16 17 18 19 20

21 22 23 24 25

(a)

25 24 23 22 21

20 19 18 17 16

15 14 13 12 11

10 9 8 7 6

5 4 3 2 1

(b)

21 16 11 6 1

22 17 12 7 2

23 18 13 8 3

24 19 14 9 4

25 20 15 10 5

(c)

Figure 1 Examples of pixel scanning order

4 Security and Communication Networks

5 Experimental Results

In this section we evaluate the performance of the proposedCPSO-based steganography method and compare it againstexisting PSO-based methods We consider two parametersfor evaluation the embedding capacity and the quality of thestego-image )e capacity is measured by the number ofsecret bits that are embedded in the cover image )e qualityof the stego-image can bemeasured by different metrics suchas PSNR and structural similarity index (SSIM)

PSNR measures the average pixel difference between thecover image and the stego-image [29] )e higher the valueof PSNR the better the quality of the stego-image )e valueof PSNR is calculated based on the value of the mean squareerror (MSE) which represents the value of the squared error

between the stego-image and the cover image )e smallerthe value of MSE the lower the error PSNR andMSE can becalculated as follows

MSE 1

W times H1113944

W

i11113936H

j1xij minus yij1113872 1113873

2PSNR 10 log

2552

MSE

(8)

where W times H is the resolution of the cover image and xij

and yij indicate the value of each pixel in the cover andstego-images

SSIM is a predictor of the perceived quality of imagesand videos It realizes image deterioration through theperceived change in the structural image data [30])e value

Table 1 Particles representation

Particle name Value range Length DescriptionDirection 0ndash15 4 bits Direction of pixel scanningX-offset 0ndash255 8 bits X-offset of starting pointY-offset 0ndash255 8 bits Y-offset of starting pointBit-planes 0ndash15 4 bits LSBs used for secret bit insertionSB-pole 0ndash1 1 bit Pole of secret bitsSB-dire 0ndash1 1 bit Direction of secret bitsBP-dire 0ndash1 1 bit Direction of bit-planesX-side-length 0ndash255 8 bits Dimension of block in x-axisY-side-length 0ndash255 1 bit Dimension of block in y-axis

Table 2 Possible values for bit-planes particle

Value Description0000 Use none of LSBs0001 Use 1st LSB0010 Use 2nd LSB0011 Use 1st and 2nd LSBs0100 Use 3rd LSB0101 Use 1st and 3rd LSBs0110 Use 2nd and 3rd LSBs0111 Use 1st 2nd and 3rd LSBs1000 Use 4th LSB1001 Use 1st and 4th LSBs1010 Use 2nd and 4th LSBs1011 Use 1st 2nd and 4th LSBs1100 Use 3rd and 4th LSBs1101 Use 1st 3rd and 4th LSBs1110 Use 2nd 3rd and 4th LSBs1111 Use four LSBs

Table 3 Possible values for SB-pole SB-dire and Bp-dire particle

Particle name Value Description

SB-pole 0 No change is made to secret bits1 All secret bits are complemented

SB-dire 0 No change is made to secret bits1 Secret bits are reversed from end to beginning

BP-dire 0 Bit-planes are used from MSB to LSB1 Bit-planes are used from LSB to MSB

Security and Communication Networks 5

of SSIM is in the range [0 1] where the value of 1 indicatesthe highest quality SSIM can be calculated between twoimage windows x and y of common size as

SSIM 2μxμy + c11113872 1113873 2σxy + c21113872 1113873

μ2x + μ2y + c11113872 1113873 σ2x + σ2y + c21113872 1113873 (9)

where μ and σ are themean and variance respectively and c1and c2 are two constants included to avoid instability

In the experiments secret images with various resolu-tions are embedded into the cover image Figure 3 shows thefive standard images used as cover and secret images Firstthe cover and secret images are divided into four separateblocks )en for each block the CPSO algorithm is applied

Apply CPSO algorithm tofind the best pixel locations

Secret bitsprocessing

Embeddingprocess

Stego-image

Extractingprocess

Secret image

Cover image Secret image

Figure 2 Data embedding and extraction

(1) Divide the cover and secret images into blocks using the X-Side-Length and Y-Side-Length particles(2) For each block convert the secret image into a sequence of bits using the SB-Pole and SB-dire particles(3) if (SB-Pole 1)(4) Complement the secret bits(5) end if(6) if (SB-dire 1)(7) Reverse the direction of the secret bits(8) end if(9) Create a sequence of cover pixels using the Direction X-offset and Y-offset particles(10) Convert the cover pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(11) Apply the CPSO algorithm (Algorithm 1)(12) if (number of secret bitslt cover pixel bits)(13) Embed secret bits into corresponding cover pixel bits(14) Embed the particles into the last row of cover image(15) Calculate PSNR of the stego-image(16) end if

ALGORITHM 2 Pseudocode of data embedding

6 Security and Communication Networks

(1) Extract the particles from the last row of stego-image(2) Prepare the image blocks using the X-Side-Length and Y-Side Length particles(3) Create a sequence of stego pixels using the Directions X-offset and Y-offset particles(4) Convert the stego pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(5) if (SB-Pole 1)(6) Complement the secret bits(7) end if(8) if (SB-dire 1)(9) Reverse the direction of the secret bits(10) end if(11) Extract the secret pixels from the secret bits(12) Render secret image using the secret pixels(13) end

ALGORITHM 3 Pseudocode of data extraction

(a) (b)

(c) (d)

Figure 3 Continued

Security and Communication Networks 7

to find the best pixel locations to embed the secret bits suchthat the PSNR of the stego-image is maximized

To study the effect of the secret image size on the quality ofthe stego-image Airplane and Baboon images with differentresolutions are used as secret images and embedded into LenaPeppers and Baboon images of resolution 256times 256 respec-tively Table 4 shows the embedding capacity for different secretimage sizes and the corresponding PSNR of the cover imagesAs shown the PSNR is decreasedwhen the embedding capacity

increases however the corresponding PSNR values are stillacceptable For example when the embedding capacity is204800 bits the PSNR values for Lena are 56145dB and601 dB respectively which are still high

)e performance of the proposed method is comparedwith two related schemes the PSO-based method in [10] andthe PSO-LSB-basedmethod in [11] Table 5 shows the resultsof embedding Lena image with resolution 256times 256 intodifferent cover images (Baboon Lena Airplane and Boat) of

Table 4 PSNR for Lena Peppers and Boat using Airplane and Baboon as secret images

Secret imageCover image Lena Peppers Boat

Resolution Capacity (bits) PSNR (dB) PSNR (dB) PSNR (dB)

Airplane

64times 64 32768 6851 6499 688480times 80 51200 67184 6316 672290times 90 64800 65823 632 661396times 96 73728 6258 6266 6563116times116 107648 6003 5930 6390128times128 131072 5942 5880 6300160times160 204800 5615 5658 6102

Baboon

64times 64 32768 6902 6894 692080times 80 51200 6737 6712 673890times 90 64800 6627 6630 662496times 96 73728 6566 6582 6569116times116 107648 6403 6102 6408128times128 131072 6015 602 6308160times160 204800 6010 5838 6120

Table 5 Comparison between CPSO PSO and PSO-LSB-based methods

Stego-imagePSNR SSIM

PSO-LSB [11] PSO [10] CPSO PSO-LSB [11] PSO [10] CPSOBaboon 511487 5784 63253 09987 09990 09998Lena 511354 5765 63029 09982 09988 09996Airplane 511448 5569 63195 09955 09971 09995Boat 511413 5589 63109 09963 09965 09973

(e)

Figure 3 Standard test images used for evaluation (a) Lena (b) Baboon (c) Peppers (d) Boat and (e) Airplane

8 Security and Communication Networks

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(a)

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(b)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(c)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(d)

3000

2000

1000

0

3000

2000

1000

0

0 50 100 150 200 250 0 50 100 150 200 250

(e)

Figure 4 Cover and stego-images with their respective histograms (a) Lena (b) Baboon (c) Boat (d) Airplane and (e) Peppers

Security and Communication Networks 9

resolution 512times 512 For each method the PSNR and SSIMof the stego-images are calculated )e results show thesuperiority of the proposed CPSO-based method over theother methods in terms of both PSNR and SSIM For ex-ample for the Lana image the CPSO has PSNR 63029 dBwhile the PSO-LSB and PSO have 511354 dB and 5765 dBrespectively In terms of SSIM the CPSO has SSIM 09996while the PSO-LSB and PSO have 09982 and 09988respectively

To further demonstrate the efficiency of the proposedmethod histogram analysis is performed on the stego-images in Table 5 Figure 4 shows the histograms of thecover and stego-images for Lena Baboon Airplane andBoat As shown the cover and stego-images are visuallyalike and the histograms of the stego-images show nonoticeable changes due to the minimal distortion of thestego pixels

6 Conclusion and Future Work

We presented an efficient steganography method for con-cealing a secret image within a cover image based on chaoticmaps and the PSO algorithm)e proposed CPSO algorithmis utilized to determine the best pixel locations in the coverimage in order to embed the secret bits with minimal dis-tortion In the proposed method we divide the cover andsecret images into four blocks to improve the embeddingcapacity )e experimental results show that the proposedmethods perform better than existing schemes in terms ofPSNR and SSIM As a future work we plan to study othertypes of chaotic maps and investigate their effect on pixelselection in order to further enhance the efficiency of theCPSO algorithm

Data Availability

)e data are available on request to the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] B B Zaidan A A Zaidan and M L Mat Kiah ldquoImpact ofdata privacy and confidentiality on developing telemedicineapplications a review participates opinion and expert con-cernsrdquo International Journal of Pharmacology vol 7 no 3pp 382ndash387 2011

[2] A K Singh J Singh and H V Singh ldquoSteganography inimages using lsb techniquerdquo International Journal of LatestTrends in Engineering and Technology (IJLTET) vol 5 no 1pp 426ndash430 2015

[3] A A Zaidan B B Zaidan A K Al-Fraja and H A JalabldquoInvestigate the capability of applying hidden data in text filean overviewrdquo Journal of Applied Sciences vol 10 no 17pp 1916ndash1922 2010

[4] M Hussain A W A Wahab Y I B Idris A T S Ho andK-H Jung ldquoImage steganography in spatial domain a

surveyrdquo Signal Processing Image Communication vol 65pp 46ndash66 2018

[5] B B Zaidan A A Zaidan A K Al-Frajat and H A JalabldquoOn the differences between hiding information and cryp-tography techniques an overviewrdquo Journal of Applied Sci-ences vol 10 no 15 pp 1650ndash1655 2010

[6] I J Kadhim P Premaratne P J Vial and B HalloranldquoComprehensive survey of image steganography techniquesevaluations and trends in future researchrdquo Neurocomputingvol 335 pp 299ndash326 2019

[7] S Kaur A Kaur and K Singh ldquoA survey of image steg-anographyrdquo IJRECE (International Journal of Research inElectronics and Computer Engineering) vol 2 no 3pp 102ndash105 2014

[8] D Wang D Tan and L Liu ldquoParticle swarm optimizationalgorithm an overviewrdquo Soft Computing vol 22 no 2pp 387ndash408 2018

[9] D Tian ldquoParticle swarm optimization with chaos-basedinitialization for numerical optimizationrdquo Intelligent Auto-mation amp Soft Computing vol 24 no 2 pp 331ndash342 2018

[10] A H Mohsin A Zaidan B Zaidan et al ldquoNew method ofimage steganography based on particle swarm optimizationalgorithm in spatial domain for high embedding capacityrdquoIEEE Access vol 7 pp 168994ndash169010 2019

[11] P K Muhuri Z Ashraf and S Goel ldquoA novel image steg-anographic method based on integer wavelet transformationand particle swarm optimizationrdquo Applied Soft Computingvol 92 Article ID 106257 2020

[12] H A Hefny and S S Azab ldquoChaotic particle swarm opti-mizationrdquo in Proceedings of the 2010 7th InternationalConference on Informatics and Systems (INFOS) pp 1ndash8Cairo Egypt March 2010

[13] Z F Yaseen and A A Kareem ldquoImage steganography basedon hybrid edge detector to hide encrypted image using ver-nam algorithmrdquo in Proceedings of the 2019 2nd ScientificConference of Computer Sciences (SCCS) pp 75ndash80 IEEEBaghdad Iraq March 2019

[14] Z Li and Y He ldquoSteganography with pixel-value differencingand modulus function based on psordquo Journal of InformationSecurity and Applications vol 43 pp 47ndash52 2018

[15] A K Sahu and G Swain ldquoPixel overlapping image steg-anography using pvd and modulus functionrdquo 3D Researchvol 9 no 3 p 40 2018

[16] Z Qu Z Li G Xu S Wu and X Wang ldquoQuantum imagesteganography protocol based on quantum image expansionand grover search algorithmrdquo IEEE Access vol 7pp 50849ndash50857 2019

[17] P D Shah and R S Bichkar ldquoA secure spatial domain imagesteganography using genetic algorithm and linear con-gruential generatorrdquo in International Conference on Intelli-gent Computing and Applications pp 119ndash129 SpringerBerlin Germany 2018

[18] R Wazirali W Alasmary M M Mahmoud and A AlhindildquoAn optimized steganography hiding capacity and imper-ceptibly using genetic algorithmsrdquo IEEE Access vol 7pp 133496ndash133508 2019

[19] S A Nie G Sulong R Ali and A Abel ldquo)e use of leastsignificant bit (lsb) and knight tour algorithm for imagesteganography of cover imagerdquo International Journal ofElectrical and Computer Engineering vol 9 no 6 p 52182019

[20] G Swain ldquoVery high capacity image steganography techniqueusing quotient value differencing and lsb substitutionrdquo

10 Security and Communication Networks

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11

by its sensitivity to the initial values If a slight change occursit leads to major changes in the behavior of the systemLogistic map is one of the simplest forms of a chaotic systemand it has been used in many fields including steganography[27 28] In this work we use the logistic map to produceprimary particles distributed in a bound structure andrecreate a specific number of particles More insights on thelogistic map are given in the next section

4 Proposed Method

One of the disadvantages of the PSO algorithm is thepremature convergence toward the optimal local solution Inthis work we propose a chaotic PSO (CPSO) algorithm tomake the PSO algorithm progressively effective in thesteganography process by locating the best pixel locations inthe cover image to embed the secret image bits whilemaintaining the quality of the stego-image resulting fromthe embedding process

41 CPSO Algorithm In the proposed algorithm we utilizethe logistic map to generate the chaotic variables and mapthese variables to the search space )e logistic equation canbe expressed as

zi+1 μzi 1 minus zi( 1113857 (3)

where zi is a chaotic variable in the interval (0 1) and μ is aparameter in the interval [0 4] that controls the behavior of

the logistic map When μ is in the range [3 4] the systembehaves in a chaotic manner

For the particles speed we utilize a sigmoid function foraccurate positioning which can be expressed by the fol-lowing equation

s vt+1i1113872 1113873

1

1 + eminus vt+1

i

(4)

In the traditional PSO algorithm random numbers areused to initialize the population particles Since the initialvelocity and direction of each particle in the population areirregular and not constant this may lead to missing somespatial positions On the other hand using a chaotic se-quence instead of the random numbers for initialization isconsidered a robust method that increases the diversity ofthe particles and enhances the performance of the PSOalgorithm by preventing premature convergence In theproposed algorithm the logistic map is applied when theparticlersquos speed and position are initialized and instead ofusing the random numbers r1 and r2 we use two chaoticsequences that are created according to the followingequations

xt+1i 0 r(t + 1)lt s v

t+1i1113872 1113873

xt+1i 1 r(t + 1)ge s v

t+1i1113872 1113873

⎧⎪⎨

⎪⎩(5)

where r(t + 1) is a real number generated between [0 1]Accordingly the particle velocity is calculated as follows

vt+1i w times v

ti + c1 times Chaotic1() times pbestti minus x

ti1113872 1113873 + c2 times Chaotic2() times gbestt minus x

ti1113872 1113873 (6)

where Chaotic is a function based on the logistic map resultsin equation (3) Algorithm 1 shows the pseudocode of theproposed CPSO algorithm

42 ScanningOrder inCover Image In this work we use LSBsubstitution to hide a secret image into the cover image Todo so we model the steganography problem as a searchproblem in which the goal is to find the best pixel locationsin the cover image to hide the secret image with minimumdistortion )is involves scanning the pixels of the coverimage and determining the order and position of the pixelsfor embedding the secret image )ere are different ordersand positions that give a different stego-image quality [10]Figure 1 shows examples of different pixel scanning ordersfor an image with dimension 5times 5 In this work we use theCPSO algorithm to find the best order and position in thecover image that maximizes the quality of the stego-image

43 Particles Representation In the CPSO algorithm nineparticles are defined that correspond to the variables shownin Table 1 [10] )e direction of pixel scanning in the coverimage consists of 16 possible states X-offset and Y-offsetrepresent the starting point position Bit-planes specify the

LSBs in the cover image that are used for embedding asshown in Table 2 [10] SB-pole and SB-dire specify the poleand direction of secret bits respectively while BP-Direspecifies the direction of the LSB planes as shown in Table 3[10]

44 Data Embedding To embed the secret message in ourcase an image S(k times v) into the cover image C(m times n) thesecret image is first divided into four separate blocks andeach block is converted into binary according to the SB-poleand SB-dire particles )en the MSB of each pixel isextracted to produce a sequence of bits with length l forembedding )e cover image is then divided into fourseparate blocks each of length w Accordingly the number ofsecret bits that can be embedded in each block can becalculated as lw After that the CPSO algorithm is appliedto find the best position of the pixels in each block Withinthe swarm the number of pixels in each block that areneeded to embed the secret bits should be determined as thenumber of pixels varies depending on the number of secretbits to be embedded Finally the number of pixel bits in thecover image is compared with the number of secret bits Ifthe number of secret bits is less than the pixel bits the secretbits are embedded into the pixels to obtain the stego-image

Security and Communication Networks 3

Otherwise the embedding process cannot be performedFigure 2 illustrates the process of data embedding andextraction

A common quality metric to measure the differencebetween the cover image and the stego-image is the peaksignal to noise ratio (PSNR) )e higher the PSNR value theless difference between the cover image and stego-image Inthe proposed CPSO algorithm the objective is to maximizethe value of PSNR In Algorithm 1 we consider the PSNRvalue as the objective function

f xi( 1113857 PSNR(C S) (7)

Accordingly the pbest and gbest values are iterativelyupdated based on the PSNR value pbest is updated when thePSNR value is greater than the previous one while gbest isupdated when the PSNR value is globally higher Algo-rithm 2 shows the pseudocode of the data embeddingprocess

45 Data Extraction To extract the secret image the par-ticles are first extracted from the last row of the stego-image)e sequence of secret bits is then extracted using thecorresponding particles to obtain the secret image Algo-rithm 3 shows the pseudocode of the data extraction process

Input Number of particles (N) maximum iterationsOutput Global best particles position (gbest)(1) for i 1 to N Initialize particles velocity and position(2) xi⟵Chaotic(xmin xmax)

(3) vi⟵Chaotic(vmin vmax)

(4) pbest⟵xi Initialize particles local best position(5) gbest⟵ ϕ Initialize particles global best position(6) end for(7) do(8) for i 1 to N

(9) if f(xi)gtf(pbesti)(10) pbesti⟵xi Update local best position(11) end if(12) if f(pbesti)gtf(gbest)(13) gbest⟵ pbesti Update global best position(14) end if(15) end for(16) for i 1 to N

(17) Update vi using equation (6)(18) Update xi using equation (2)(19) end for(20) while (Maximum iterations or stopping criterion is not attained)

ALGORITHM 1 Pseudocode of the proposed CPSO algorithm

1 2 3 4 5

6 7 8 9 10

11 12 13 14 15

16 17 18 19 20

21 22 23 24 25

(a)

25 24 23 22 21

20 19 18 17 16

15 14 13 12 11

10 9 8 7 6

5 4 3 2 1

(b)

21 16 11 6 1

22 17 12 7 2

23 18 13 8 3

24 19 14 9 4

25 20 15 10 5

(c)

Figure 1 Examples of pixel scanning order

4 Security and Communication Networks

5 Experimental Results

In this section we evaluate the performance of the proposedCPSO-based steganography method and compare it againstexisting PSO-based methods We consider two parametersfor evaluation the embedding capacity and the quality of thestego-image )e capacity is measured by the number ofsecret bits that are embedded in the cover image )e qualityof the stego-image can bemeasured by different metrics suchas PSNR and structural similarity index (SSIM)

PSNR measures the average pixel difference between thecover image and the stego-image [29] )e higher the valueof PSNR the better the quality of the stego-image )e valueof PSNR is calculated based on the value of the mean squareerror (MSE) which represents the value of the squared error

between the stego-image and the cover image )e smallerthe value of MSE the lower the error PSNR andMSE can becalculated as follows

MSE 1

W times H1113944

W

i11113936H

j1xij minus yij1113872 1113873

2PSNR 10 log

2552

MSE

(8)

where W times H is the resolution of the cover image and xij

and yij indicate the value of each pixel in the cover andstego-images

SSIM is a predictor of the perceived quality of imagesand videos It realizes image deterioration through theperceived change in the structural image data [30])e value

Table 1 Particles representation

Particle name Value range Length DescriptionDirection 0ndash15 4 bits Direction of pixel scanningX-offset 0ndash255 8 bits X-offset of starting pointY-offset 0ndash255 8 bits Y-offset of starting pointBit-planes 0ndash15 4 bits LSBs used for secret bit insertionSB-pole 0ndash1 1 bit Pole of secret bitsSB-dire 0ndash1 1 bit Direction of secret bitsBP-dire 0ndash1 1 bit Direction of bit-planesX-side-length 0ndash255 8 bits Dimension of block in x-axisY-side-length 0ndash255 1 bit Dimension of block in y-axis

Table 2 Possible values for bit-planes particle

Value Description0000 Use none of LSBs0001 Use 1st LSB0010 Use 2nd LSB0011 Use 1st and 2nd LSBs0100 Use 3rd LSB0101 Use 1st and 3rd LSBs0110 Use 2nd and 3rd LSBs0111 Use 1st 2nd and 3rd LSBs1000 Use 4th LSB1001 Use 1st and 4th LSBs1010 Use 2nd and 4th LSBs1011 Use 1st 2nd and 4th LSBs1100 Use 3rd and 4th LSBs1101 Use 1st 3rd and 4th LSBs1110 Use 2nd 3rd and 4th LSBs1111 Use four LSBs

Table 3 Possible values for SB-pole SB-dire and Bp-dire particle

Particle name Value Description

SB-pole 0 No change is made to secret bits1 All secret bits are complemented

SB-dire 0 No change is made to secret bits1 Secret bits are reversed from end to beginning

BP-dire 0 Bit-planes are used from MSB to LSB1 Bit-planes are used from LSB to MSB

Security and Communication Networks 5

of SSIM is in the range [0 1] where the value of 1 indicatesthe highest quality SSIM can be calculated between twoimage windows x and y of common size as

SSIM 2μxμy + c11113872 1113873 2σxy + c21113872 1113873

μ2x + μ2y + c11113872 1113873 σ2x + σ2y + c21113872 1113873 (9)

where μ and σ are themean and variance respectively and c1and c2 are two constants included to avoid instability

In the experiments secret images with various resolu-tions are embedded into the cover image Figure 3 shows thefive standard images used as cover and secret images Firstthe cover and secret images are divided into four separateblocks )en for each block the CPSO algorithm is applied

Apply CPSO algorithm tofind the best pixel locations

Secret bitsprocessing

Embeddingprocess

Stego-image

Extractingprocess

Secret image

Cover image Secret image

Figure 2 Data embedding and extraction

(1) Divide the cover and secret images into blocks using the X-Side-Length and Y-Side-Length particles(2) For each block convert the secret image into a sequence of bits using the SB-Pole and SB-dire particles(3) if (SB-Pole 1)(4) Complement the secret bits(5) end if(6) if (SB-dire 1)(7) Reverse the direction of the secret bits(8) end if(9) Create a sequence of cover pixels using the Direction X-offset and Y-offset particles(10) Convert the cover pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(11) Apply the CPSO algorithm (Algorithm 1)(12) if (number of secret bitslt cover pixel bits)(13) Embed secret bits into corresponding cover pixel bits(14) Embed the particles into the last row of cover image(15) Calculate PSNR of the stego-image(16) end if

ALGORITHM 2 Pseudocode of data embedding

6 Security and Communication Networks

(1) Extract the particles from the last row of stego-image(2) Prepare the image blocks using the X-Side-Length and Y-Side Length particles(3) Create a sequence of stego pixels using the Directions X-offset and Y-offset particles(4) Convert the stego pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(5) if (SB-Pole 1)(6) Complement the secret bits(7) end if(8) if (SB-dire 1)(9) Reverse the direction of the secret bits(10) end if(11) Extract the secret pixels from the secret bits(12) Render secret image using the secret pixels(13) end

ALGORITHM 3 Pseudocode of data extraction

(a) (b)

(c) (d)

Figure 3 Continued

Security and Communication Networks 7

to find the best pixel locations to embed the secret bits suchthat the PSNR of the stego-image is maximized

To study the effect of the secret image size on the quality ofthe stego-image Airplane and Baboon images with differentresolutions are used as secret images and embedded into LenaPeppers and Baboon images of resolution 256times 256 respec-tively Table 4 shows the embedding capacity for different secretimage sizes and the corresponding PSNR of the cover imagesAs shown the PSNR is decreasedwhen the embedding capacity

increases however the corresponding PSNR values are stillacceptable For example when the embedding capacity is204800 bits the PSNR values for Lena are 56145dB and601 dB respectively which are still high

)e performance of the proposed method is comparedwith two related schemes the PSO-based method in [10] andthe PSO-LSB-basedmethod in [11] Table 5 shows the resultsof embedding Lena image with resolution 256times 256 intodifferent cover images (Baboon Lena Airplane and Boat) of

Table 4 PSNR for Lena Peppers and Boat using Airplane and Baboon as secret images

Secret imageCover image Lena Peppers Boat

Resolution Capacity (bits) PSNR (dB) PSNR (dB) PSNR (dB)

Airplane

64times 64 32768 6851 6499 688480times 80 51200 67184 6316 672290times 90 64800 65823 632 661396times 96 73728 6258 6266 6563116times116 107648 6003 5930 6390128times128 131072 5942 5880 6300160times160 204800 5615 5658 6102

Baboon

64times 64 32768 6902 6894 692080times 80 51200 6737 6712 673890times 90 64800 6627 6630 662496times 96 73728 6566 6582 6569116times116 107648 6403 6102 6408128times128 131072 6015 602 6308160times160 204800 6010 5838 6120

Table 5 Comparison between CPSO PSO and PSO-LSB-based methods

Stego-imagePSNR SSIM

PSO-LSB [11] PSO [10] CPSO PSO-LSB [11] PSO [10] CPSOBaboon 511487 5784 63253 09987 09990 09998Lena 511354 5765 63029 09982 09988 09996Airplane 511448 5569 63195 09955 09971 09995Boat 511413 5589 63109 09963 09965 09973

(e)

Figure 3 Standard test images used for evaluation (a) Lena (b) Baboon (c) Peppers (d) Boat and (e) Airplane

8 Security and Communication Networks

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(a)

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(b)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(c)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(d)

3000

2000

1000

0

3000

2000

1000

0

0 50 100 150 200 250 0 50 100 150 200 250

(e)

Figure 4 Cover and stego-images with their respective histograms (a) Lena (b) Baboon (c) Boat (d) Airplane and (e) Peppers

Security and Communication Networks 9

resolution 512times 512 For each method the PSNR and SSIMof the stego-images are calculated )e results show thesuperiority of the proposed CPSO-based method over theother methods in terms of both PSNR and SSIM For ex-ample for the Lana image the CPSO has PSNR 63029 dBwhile the PSO-LSB and PSO have 511354 dB and 5765 dBrespectively In terms of SSIM the CPSO has SSIM 09996while the PSO-LSB and PSO have 09982 and 09988respectively

To further demonstrate the efficiency of the proposedmethod histogram analysis is performed on the stego-images in Table 5 Figure 4 shows the histograms of thecover and stego-images for Lena Baboon Airplane andBoat As shown the cover and stego-images are visuallyalike and the histograms of the stego-images show nonoticeable changes due to the minimal distortion of thestego pixels

6 Conclusion and Future Work

We presented an efficient steganography method for con-cealing a secret image within a cover image based on chaoticmaps and the PSO algorithm)e proposed CPSO algorithmis utilized to determine the best pixel locations in the coverimage in order to embed the secret bits with minimal dis-tortion In the proposed method we divide the cover andsecret images into four blocks to improve the embeddingcapacity )e experimental results show that the proposedmethods perform better than existing schemes in terms ofPSNR and SSIM As a future work we plan to study othertypes of chaotic maps and investigate their effect on pixelselection in order to further enhance the efficiency of theCPSO algorithm

Data Availability

)e data are available on request to the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] B B Zaidan A A Zaidan and M L Mat Kiah ldquoImpact ofdata privacy and confidentiality on developing telemedicineapplications a review participates opinion and expert con-cernsrdquo International Journal of Pharmacology vol 7 no 3pp 382ndash387 2011

[2] A K Singh J Singh and H V Singh ldquoSteganography inimages using lsb techniquerdquo International Journal of LatestTrends in Engineering and Technology (IJLTET) vol 5 no 1pp 426ndash430 2015

[3] A A Zaidan B B Zaidan A K Al-Fraja and H A JalabldquoInvestigate the capability of applying hidden data in text filean overviewrdquo Journal of Applied Sciences vol 10 no 17pp 1916ndash1922 2010

[4] M Hussain A W A Wahab Y I B Idris A T S Ho andK-H Jung ldquoImage steganography in spatial domain a

surveyrdquo Signal Processing Image Communication vol 65pp 46ndash66 2018

[5] B B Zaidan A A Zaidan A K Al-Frajat and H A JalabldquoOn the differences between hiding information and cryp-tography techniques an overviewrdquo Journal of Applied Sci-ences vol 10 no 15 pp 1650ndash1655 2010

[6] I J Kadhim P Premaratne P J Vial and B HalloranldquoComprehensive survey of image steganography techniquesevaluations and trends in future researchrdquo Neurocomputingvol 335 pp 299ndash326 2019

[7] S Kaur A Kaur and K Singh ldquoA survey of image steg-anographyrdquo IJRECE (International Journal of Research inElectronics and Computer Engineering) vol 2 no 3pp 102ndash105 2014

[8] D Wang D Tan and L Liu ldquoParticle swarm optimizationalgorithm an overviewrdquo Soft Computing vol 22 no 2pp 387ndash408 2018

[9] D Tian ldquoParticle swarm optimization with chaos-basedinitialization for numerical optimizationrdquo Intelligent Auto-mation amp Soft Computing vol 24 no 2 pp 331ndash342 2018

[10] A H Mohsin A Zaidan B Zaidan et al ldquoNew method ofimage steganography based on particle swarm optimizationalgorithm in spatial domain for high embedding capacityrdquoIEEE Access vol 7 pp 168994ndash169010 2019

[11] P K Muhuri Z Ashraf and S Goel ldquoA novel image steg-anographic method based on integer wavelet transformationand particle swarm optimizationrdquo Applied Soft Computingvol 92 Article ID 106257 2020

[12] H A Hefny and S S Azab ldquoChaotic particle swarm opti-mizationrdquo in Proceedings of the 2010 7th InternationalConference on Informatics and Systems (INFOS) pp 1ndash8Cairo Egypt March 2010

[13] Z F Yaseen and A A Kareem ldquoImage steganography basedon hybrid edge detector to hide encrypted image using ver-nam algorithmrdquo in Proceedings of the 2019 2nd ScientificConference of Computer Sciences (SCCS) pp 75ndash80 IEEEBaghdad Iraq March 2019

[14] Z Li and Y He ldquoSteganography with pixel-value differencingand modulus function based on psordquo Journal of InformationSecurity and Applications vol 43 pp 47ndash52 2018

[15] A K Sahu and G Swain ldquoPixel overlapping image steg-anography using pvd and modulus functionrdquo 3D Researchvol 9 no 3 p 40 2018

[16] Z Qu Z Li G Xu S Wu and X Wang ldquoQuantum imagesteganography protocol based on quantum image expansionand grover search algorithmrdquo IEEE Access vol 7pp 50849ndash50857 2019

[17] P D Shah and R S Bichkar ldquoA secure spatial domain imagesteganography using genetic algorithm and linear con-gruential generatorrdquo in International Conference on Intelli-gent Computing and Applications pp 119ndash129 SpringerBerlin Germany 2018

[18] R Wazirali W Alasmary M M Mahmoud and A AlhindildquoAn optimized steganography hiding capacity and imper-ceptibly using genetic algorithmsrdquo IEEE Access vol 7pp 133496ndash133508 2019

[19] S A Nie G Sulong R Ali and A Abel ldquo)e use of leastsignificant bit (lsb) and knight tour algorithm for imagesteganography of cover imagerdquo International Journal ofElectrical and Computer Engineering vol 9 no 6 p 52182019

[20] G Swain ldquoVery high capacity image steganography techniqueusing quotient value differencing and lsb substitutionrdquo

10 Security and Communication Networks

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11

Otherwise the embedding process cannot be performedFigure 2 illustrates the process of data embedding andextraction

A common quality metric to measure the differencebetween the cover image and the stego-image is the peaksignal to noise ratio (PSNR) )e higher the PSNR value theless difference between the cover image and stego-image Inthe proposed CPSO algorithm the objective is to maximizethe value of PSNR In Algorithm 1 we consider the PSNRvalue as the objective function

f xi( 1113857 PSNR(C S) (7)

Accordingly the pbest and gbest values are iterativelyupdated based on the PSNR value pbest is updated when thePSNR value is greater than the previous one while gbest isupdated when the PSNR value is globally higher Algo-rithm 2 shows the pseudocode of the data embeddingprocess

45 Data Extraction To extract the secret image the par-ticles are first extracted from the last row of the stego-image)e sequence of secret bits is then extracted using thecorresponding particles to obtain the secret image Algo-rithm 3 shows the pseudocode of the data extraction process

Input Number of particles (N) maximum iterationsOutput Global best particles position (gbest)(1) for i 1 to N Initialize particles velocity and position(2) xi⟵Chaotic(xmin xmax)

(3) vi⟵Chaotic(vmin vmax)

(4) pbest⟵xi Initialize particles local best position(5) gbest⟵ ϕ Initialize particles global best position(6) end for(7) do(8) for i 1 to N

(9) if f(xi)gtf(pbesti)(10) pbesti⟵xi Update local best position(11) end if(12) if f(pbesti)gtf(gbest)(13) gbest⟵ pbesti Update global best position(14) end if(15) end for(16) for i 1 to N

(17) Update vi using equation (6)(18) Update xi using equation (2)(19) end for(20) while (Maximum iterations or stopping criterion is not attained)

ALGORITHM 1 Pseudocode of the proposed CPSO algorithm

1 2 3 4 5

6 7 8 9 10

11 12 13 14 15

16 17 18 19 20

21 22 23 24 25

(a)

25 24 23 22 21

20 19 18 17 16

15 14 13 12 11

10 9 8 7 6

5 4 3 2 1

(b)

21 16 11 6 1

22 17 12 7 2

23 18 13 8 3

24 19 14 9 4

25 20 15 10 5

(c)

Figure 1 Examples of pixel scanning order

4 Security and Communication Networks

5 Experimental Results

In this section we evaluate the performance of the proposedCPSO-based steganography method and compare it againstexisting PSO-based methods We consider two parametersfor evaluation the embedding capacity and the quality of thestego-image )e capacity is measured by the number ofsecret bits that are embedded in the cover image )e qualityof the stego-image can bemeasured by different metrics suchas PSNR and structural similarity index (SSIM)

PSNR measures the average pixel difference between thecover image and the stego-image [29] )e higher the valueof PSNR the better the quality of the stego-image )e valueof PSNR is calculated based on the value of the mean squareerror (MSE) which represents the value of the squared error

between the stego-image and the cover image )e smallerthe value of MSE the lower the error PSNR andMSE can becalculated as follows

MSE 1

W times H1113944

W

i11113936H

j1xij minus yij1113872 1113873

2PSNR 10 log

2552

MSE

(8)

where W times H is the resolution of the cover image and xij

and yij indicate the value of each pixel in the cover andstego-images

SSIM is a predictor of the perceived quality of imagesand videos It realizes image deterioration through theperceived change in the structural image data [30])e value

Table 1 Particles representation

Particle name Value range Length DescriptionDirection 0ndash15 4 bits Direction of pixel scanningX-offset 0ndash255 8 bits X-offset of starting pointY-offset 0ndash255 8 bits Y-offset of starting pointBit-planes 0ndash15 4 bits LSBs used for secret bit insertionSB-pole 0ndash1 1 bit Pole of secret bitsSB-dire 0ndash1 1 bit Direction of secret bitsBP-dire 0ndash1 1 bit Direction of bit-planesX-side-length 0ndash255 8 bits Dimension of block in x-axisY-side-length 0ndash255 1 bit Dimension of block in y-axis

Table 2 Possible values for bit-planes particle

Value Description0000 Use none of LSBs0001 Use 1st LSB0010 Use 2nd LSB0011 Use 1st and 2nd LSBs0100 Use 3rd LSB0101 Use 1st and 3rd LSBs0110 Use 2nd and 3rd LSBs0111 Use 1st 2nd and 3rd LSBs1000 Use 4th LSB1001 Use 1st and 4th LSBs1010 Use 2nd and 4th LSBs1011 Use 1st 2nd and 4th LSBs1100 Use 3rd and 4th LSBs1101 Use 1st 3rd and 4th LSBs1110 Use 2nd 3rd and 4th LSBs1111 Use four LSBs

Table 3 Possible values for SB-pole SB-dire and Bp-dire particle

Particle name Value Description

SB-pole 0 No change is made to secret bits1 All secret bits are complemented

SB-dire 0 No change is made to secret bits1 Secret bits are reversed from end to beginning

BP-dire 0 Bit-planes are used from MSB to LSB1 Bit-planes are used from LSB to MSB

Security and Communication Networks 5

of SSIM is in the range [0 1] where the value of 1 indicatesthe highest quality SSIM can be calculated between twoimage windows x and y of common size as

SSIM 2μxμy + c11113872 1113873 2σxy + c21113872 1113873

μ2x + μ2y + c11113872 1113873 σ2x + σ2y + c21113872 1113873 (9)

where μ and σ are themean and variance respectively and c1and c2 are two constants included to avoid instability

In the experiments secret images with various resolu-tions are embedded into the cover image Figure 3 shows thefive standard images used as cover and secret images Firstthe cover and secret images are divided into four separateblocks )en for each block the CPSO algorithm is applied

Apply CPSO algorithm tofind the best pixel locations

Secret bitsprocessing

Embeddingprocess

Stego-image

Extractingprocess

Secret image

Cover image Secret image

Figure 2 Data embedding and extraction

(1) Divide the cover and secret images into blocks using the X-Side-Length and Y-Side-Length particles(2) For each block convert the secret image into a sequence of bits using the SB-Pole and SB-dire particles(3) if (SB-Pole 1)(4) Complement the secret bits(5) end if(6) if (SB-dire 1)(7) Reverse the direction of the secret bits(8) end if(9) Create a sequence of cover pixels using the Direction X-offset and Y-offset particles(10) Convert the cover pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(11) Apply the CPSO algorithm (Algorithm 1)(12) if (number of secret bitslt cover pixel bits)(13) Embed secret bits into corresponding cover pixel bits(14) Embed the particles into the last row of cover image(15) Calculate PSNR of the stego-image(16) end if

ALGORITHM 2 Pseudocode of data embedding

6 Security and Communication Networks

(1) Extract the particles from the last row of stego-image(2) Prepare the image blocks using the X-Side-Length and Y-Side Length particles(3) Create a sequence of stego pixels using the Directions X-offset and Y-offset particles(4) Convert the stego pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(5) if (SB-Pole 1)(6) Complement the secret bits(7) end if(8) if (SB-dire 1)(9) Reverse the direction of the secret bits(10) end if(11) Extract the secret pixels from the secret bits(12) Render secret image using the secret pixels(13) end

ALGORITHM 3 Pseudocode of data extraction

(a) (b)

(c) (d)

Figure 3 Continued

Security and Communication Networks 7

to find the best pixel locations to embed the secret bits suchthat the PSNR of the stego-image is maximized

To study the effect of the secret image size on the quality ofthe stego-image Airplane and Baboon images with differentresolutions are used as secret images and embedded into LenaPeppers and Baboon images of resolution 256times 256 respec-tively Table 4 shows the embedding capacity for different secretimage sizes and the corresponding PSNR of the cover imagesAs shown the PSNR is decreasedwhen the embedding capacity

increases however the corresponding PSNR values are stillacceptable For example when the embedding capacity is204800 bits the PSNR values for Lena are 56145dB and601 dB respectively which are still high

)e performance of the proposed method is comparedwith two related schemes the PSO-based method in [10] andthe PSO-LSB-basedmethod in [11] Table 5 shows the resultsof embedding Lena image with resolution 256times 256 intodifferent cover images (Baboon Lena Airplane and Boat) of

Table 4 PSNR for Lena Peppers and Boat using Airplane and Baboon as secret images

Secret imageCover image Lena Peppers Boat

Resolution Capacity (bits) PSNR (dB) PSNR (dB) PSNR (dB)

Airplane

64times 64 32768 6851 6499 688480times 80 51200 67184 6316 672290times 90 64800 65823 632 661396times 96 73728 6258 6266 6563116times116 107648 6003 5930 6390128times128 131072 5942 5880 6300160times160 204800 5615 5658 6102

Baboon

64times 64 32768 6902 6894 692080times 80 51200 6737 6712 673890times 90 64800 6627 6630 662496times 96 73728 6566 6582 6569116times116 107648 6403 6102 6408128times128 131072 6015 602 6308160times160 204800 6010 5838 6120

Table 5 Comparison between CPSO PSO and PSO-LSB-based methods

Stego-imagePSNR SSIM

PSO-LSB [11] PSO [10] CPSO PSO-LSB [11] PSO [10] CPSOBaboon 511487 5784 63253 09987 09990 09998Lena 511354 5765 63029 09982 09988 09996Airplane 511448 5569 63195 09955 09971 09995Boat 511413 5589 63109 09963 09965 09973

(e)

Figure 3 Standard test images used for evaluation (a) Lena (b) Baboon (c) Peppers (d) Boat and (e) Airplane

8 Security and Communication Networks

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(a)

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(b)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(c)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(d)

3000

2000

1000

0

3000

2000

1000

0

0 50 100 150 200 250 0 50 100 150 200 250

(e)

Figure 4 Cover and stego-images with their respective histograms (a) Lena (b) Baboon (c) Boat (d) Airplane and (e) Peppers

Security and Communication Networks 9

resolution 512times 512 For each method the PSNR and SSIMof the stego-images are calculated )e results show thesuperiority of the proposed CPSO-based method over theother methods in terms of both PSNR and SSIM For ex-ample for the Lana image the CPSO has PSNR 63029 dBwhile the PSO-LSB and PSO have 511354 dB and 5765 dBrespectively In terms of SSIM the CPSO has SSIM 09996while the PSO-LSB and PSO have 09982 and 09988respectively

To further demonstrate the efficiency of the proposedmethod histogram analysis is performed on the stego-images in Table 5 Figure 4 shows the histograms of thecover and stego-images for Lena Baboon Airplane andBoat As shown the cover and stego-images are visuallyalike and the histograms of the stego-images show nonoticeable changes due to the minimal distortion of thestego pixels

6 Conclusion and Future Work

We presented an efficient steganography method for con-cealing a secret image within a cover image based on chaoticmaps and the PSO algorithm)e proposed CPSO algorithmis utilized to determine the best pixel locations in the coverimage in order to embed the secret bits with minimal dis-tortion In the proposed method we divide the cover andsecret images into four blocks to improve the embeddingcapacity )e experimental results show that the proposedmethods perform better than existing schemes in terms ofPSNR and SSIM As a future work we plan to study othertypes of chaotic maps and investigate their effect on pixelselection in order to further enhance the efficiency of theCPSO algorithm

Data Availability

)e data are available on request to the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] B B Zaidan A A Zaidan and M L Mat Kiah ldquoImpact ofdata privacy and confidentiality on developing telemedicineapplications a review participates opinion and expert con-cernsrdquo International Journal of Pharmacology vol 7 no 3pp 382ndash387 2011

[2] A K Singh J Singh and H V Singh ldquoSteganography inimages using lsb techniquerdquo International Journal of LatestTrends in Engineering and Technology (IJLTET) vol 5 no 1pp 426ndash430 2015

[3] A A Zaidan B B Zaidan A K Al-Fraja and H A JalabldquoInvestigate the capability of applying hidden data in text filean overviewrdquo Journal of Applied Sciences vol 10 no 17pp 1916ndash1922 2010

[4] M Hussain A W A Wahab Y I B Idris A T S Ho andK-H Jung ldquoImage steganography in spatial domain a

surveyrdquo Signal Processing Image Communication vol 65pp 46ndash66 2018

[5] B B Zaidan A A Zaidan A K Al-Frajat and H A JalabldquoOn the differences between hiding information and cryp-tography techniques an overviewrdquo Journal of Applied Sci-ences vol 10 no 15 pp 1650ndash1655 2010

[6] I J Kadhim P Premaratne P J Vial and B HalloranldquoComprehensive survey of image steganography techniquesevaluations and trends in future researchrdquo Neurocomputingvol 335 pp 299ndash326 2019

[7] S Kaur A Kaur and K Singh ldquoA survey of image steg-anographyrdquo IJRECE (International Journal of Research inElectronics and Computer Engineering) vol 2 no 3pp 102ndash105 2014

[8] D Wang D Tan and L Liu ldquoParticle swarm optimizationalgorithm an overviewrdquo Soft Computing vol 22 no 2pp 387ndash408 2018

[9] D Tian ldquoParticle swarm optimization with chaos-basedinitialization for numerical optimizationrdquo Intelligent Auto-mation amp Soft Computing vol 24 no 2 pp 331ndash342 2018

[10] A H Mohsin A Zaidan B Zaidan et al ldquoNew method ofimage steganography based on particle swarm optimizationalgorithm in spatial domain for high embedding capacityrdquoIEEE Access vol 7 pp 168994ndash169010 2019

[11] P K Muhuri Z Ashraf and S Goel ldquoA novel image steg-anographic method based on integer wavelet transformationand particle swarm optimizationrdquo Applied Soft Computingvol 92 Article ID 106257 2020

[12] H A Hefny and S S Azab ldquoChaotic particle swarm opti-mizationrdquo in Proceedings of the 2010 7th InternationalConference on Informatics and Systems (INFOS) pp 1ndash8Cairo Egypt March 2010

[13] Z F Yaseen and A A Kareem ldquoImage steganography basedon hybrid edge detector to hide encrypted image using ver-nam algorithmrdquo in Proceedings of the 2019 2nd ScientificConference of Computer Sciences (SCCS) pp 75ndash80 IEEEBaghdad Iraq March 2019

[14] Z Li and Y He ldquoSteganography with pixel-value differencingand modulus function based on psordquo Journal of InformationSecurity and Applications vol 43 pp 47ndash52 2018

[15] A K Sahu and G Swain ldquoPixel overlapping image steg-anography using pvd and modulus functionrdquo 3D Researchvol 9 no 3 p 40 2018

[16] Z Qu Z Li G Xu S Wu and X Wang ldquoQuantum imagesteganography protocol based on quantum image expansionand grover search algorithmrdquo IEEE Access vol 7pp 50849ndash50857 2019

[17] P D Shah and R S Bichkar ldquoA secure spatial domain imagesteganography using genetic algorithm and linear con-gruential generatorrdquo in International Conference on Intelli-gent Computing and Applications pp 119ndash129 SpringerBerlin Germany 2018

[18] R Wazirali W Alasmary M M Mahmoud and A AlhindildquoAn optimized steganography hiding capacity and imper-ceptibly using genetic algorithmsrdquo IEEE Access vol 7pp 133496ndash133508 2019

[19] S A Nie G Sulong R Ali and A Abel ldquo)e use of leastsignificant bit (lsb) and knight tour algorithm for imagesteganography of cover imagerdquo International Journal ofElectrical and Computer Engineering vol 9 no 6 p 52182019

[20] G Swain ldquoVery high capacity image steganography techniqueusing quotient value differencing and lsb substitutionrdquo

10 Security and Communication Networks

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11

5 Experimental Results

In this section we evaluate the performance of the proposedCPSO-based steganography method and compare it againstexisting PSO-based methods We consider two parametersfor evaluation the embedding capacity and the quality of thestego-image )e capacity is measured by the number ofsecret bits that are embedded in the cover image )e qualityof the stego-image can bemeasured by different metrics suchas PSNR and structural similarity index (SSIM)

PSNR measures the average pixel difference between thecover image and the stego-image [29] )e higher the valueof PSNR the better the quality of the stego-image )e valueof PSNR is calculated based on the value of the mean squareerror (MSE) which represents the value of the squared error

between the stego-image and the cover image )e smallerthe value of MSE the lower the error PSNR andMSE can becalculated as follows

MSE 1

W times H1113944

W

i11113936H

j1xij minus yij1113872 1113873

2PSNR 10 log

2552

MSE

(8)

where W times H is the resolution of the cover image and xij

and yij indicate the value of each pixel in the cover andstego-images

SSIM is a predictor of the perceived quality of imagesand videos It realizes image deterioration through theperceived change in the structural image data [30])e value

Table 1 Particles representation

Particle name Value range Length DescriptionDirection 0ndash15 4 bits Direction of pixel scanningX-offset 0ndash255 8 bits X-offset of starting pointY-offset 0ndash255 8 bits Y-offset of starting pointBit-planes 0ndash15 4 bits LSBs used for secret bit insertionSB-pole 0ndash1 1 bit Pole of secret bitsSB-dire 0ndash1 1 bit Direction of secret bitsBP-dire 0ndash1 1 bit Direction of bit-planesX-side-length 0ndash255 8 bits Dimension of block in x-axisY-side-length 0ndash255 1 bit Dimension of block in y-axis

Table 2 Possible values for bit-planes particle

Value Description0000 Use none of LSBs0001 Use 1st LSB0010 Use 2nd LSB0011 Use 1st and 2nd LSBs0100 Use 3rd LSB0101 Use 1st and 3rd LSBs0110 Use 2nd and 3rd LSBs0111 Use 1st 2nd and 3rd LSBs1000 Use 4th LSB1001 Use 1st and 4th LSBs1010 Use 2nd and 4th LSBs1011 Use 1st 2nd and 4th LSBs1100 Use 3rd and 4th LSBs1101 Use 1st 3rd and 4th LSBs1110 Use 2nd 3rd and 4th LSBs1111 Use four LSBs

Table 3 Possible values for SB-pole SB-dire and Bp-dire particle

Particle name Value Description

SB-pole 0 No change is made to secret bits1 All secret bits are complemented

SB-dire 0 No change is made to secret bits1 Secret bits are reversed from end to beginning

BP-dire 0 Bit-planes are used from MSB to LSB1 Bit-planes are used from LSB to MSB

Security and Communication Networks 5

of SSIM is in the range [0 1] where the value of 1 indicatesthe highest quality SSIM can be calculated between twoimage windows x and y of common size as

SSIM 2μxμy + c11113872 1113873 2σxy + c21113872 1113873

μ2x + μ2y + c11113872 1113873 σ2x + σ2y + c21113872 1113873 (9)

where μ and σ are themean and variance respectively and c1and c2 are two constants included to avoid instability

In the experiments secret images with various resolu-tions are embedded into the cover image Figure 3 shows thefive standard images used as cover and secret images Firstthe cover and secret images are divided into four separateblocks )en for each block the CPSO algorithm is applied

Apply CPSO algorithm tofind the best pixel locations

Secret bitsprocessing

Embeddingprocess

Stego-image

Extractingprocess

Secret image

Cover image Secret image

Figure 2 Data embedding and extraction

(1) Divide the cover and secret images into blocks using the X-Side-Length and Y-Side-Length particles(2) For each block convert the secret image into a sequence of bits using the SB-Pole and SB-dire particles(3) if (SB-Pole 1)(4) Complement the secret bits(5) end if(6) if (SB-dire 1)(7) Reverse the direction of the secret bits(8) end if(9) Create a sequence of cover pixels using the Direction X-offset and Y-offset particles(10) Convert the cover pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(11) Apply the CPSO algorithm (Algorithm 1)(12) if (number of secret bitslt cover pixel bits)(13) Embed secret bits into corresponding cover pixel bits(14) Embed the particles into the last row of cover image(15) Calculate PSNR of the stego-image(16) end if

ALGORITHM 2 Pseudocode of data embedding

6 Security and Communication Networks

(1) Extract the particles from the last row of stego-image(2) Prepare the image blocks using the X-Side-Length and Y-Side Length particles(3) Create a sequence of stego pixels using the Directions X-offset and Y-offset particles(4) Convert the stego pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(5) if (SB-Pole 1)(6) Complement the secret bits(7) end if(8) if (SB-dire 1)(9) Reverse the direction of the secret bits(10) end if(11) Extract the secret pixels from the secret bits(12) Render secret image using the secret pixels(13) end

ALGORITHM 3 Pseudocode of data extraction

(a) (b)

(c) (d)

Figure 3 Continued

Security and Communication Networks 7

to find the best pixel locations to embed the secret bits suchthat the PSNR of the stego-image is maximized

To study the effect of the secret image size on the quality ofthe stego-image Airplane and Baboon images with differentresolutions are used as secret images and embedded into LenaPeppers and Baboon images of resolution 256times 256 respec-tively Table 4 shows the embedding capacity for different secretimage sizes and the corresponding PSNR of the cover imagesAs shown the PSNR is decreasedwhen the embedding capacity

increases however the corresponding PSNR values are stillacceptable For example when the embedding capacity is204800 bits the PSNR values for Lena are 56145dB and601 dB respectively which are still high

)e performance of the proposed method is comparedwith two related schemes the PSO-based method in [10] andthe PSO-LSB-basedmethod in [11] Table 5 shows the resultsof embedding Lena image with resolution 256times 256 intodifferent cover images (Baboon Lena Airplane and Boat) of

Table 4 PSNR for Lena Peppers and Boat using Airplane and Baboon as secret images

Secret imageCover image Lena Peppers Boat

Resolution Capacity (bits) PSNR (dB) PSNR (dB) PSNR (dB)

Airplane

64times 64 32768 6851 6499 688480times 80 51200 67184 6316 672290times 90 64800 65823 632 661396times 96 73728 6258 6266 6563116times116 107648 6003 5930 6390128times128 131072 5942 5880 6300160times160 204800 5615 5658 6102

Baboon

64times 64 32768 6902 6894 692080times 80 51200 6737 6712 673890times 90 64800 6627 6630 662496times 96 73728 6566 6582 6569116times116 107648 6403 6102 6408128times128 131072 6015 602 6308160times160 204800 6010 5838 6120

Table 5 Comparison between CPSO PSO and PSO-LSB-based methods

Stego-imagePSNR SSIM

PSO-LSB [11] PSO [10] CPSO PSO-LSB [11] PSO [10] CPSOBaboon 511487 5784 63253 09987 09990 09998Lena 511354 5765 63029 09982 09988 09996Airplane 511448 5569 63195 09955 09971 09995Boat 511413 5589 63109 09963 09965 09973

(e)

Figure 3 Standard test images used for evaluation (a) Lena (b) Baboon (c) Peppers (d) Boat and (e) Airplane

8 Security and Communication Networks

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(a)

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(b)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(c)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(d)

3000

2000

1000

0

3000

2000

1000

0

0 50 100 150 200 250 0 50 100 150 200 250

(e)

Figure 4 Cover and stego-images with their respective histograms (a) Lena (b) Baboon (c) Boat (d) Airplane and (e) Peppers

Security and Communication Networks 9

resolution 512times 512 For each method the PSNR and SSIMof the stego-images are calculated )e results show thesuperiority of the proposed CPSO-based method over theother methods in terms of both PSNR and SSIM For ex-ample for the Lana image the CPSO has PSNR 63029 dBwhile the PSO-LSB and PSO have 511354 dB and 5765 dBrespectively In terms of SSIM the CPSO has SSIM 09996while the PSO-LSB and PSO have 09982 and 09988respectively

To further demonstrate the efficiency of the proposedmethod histogram analysis is performed on the stego-images in Table 5 Figure 4 shows the histograms of thecover and stego-images for Lena Baboon Airplane andBoat As shown the cover and stego-images are visuallyalike and the histograms of the stego-images show nonoticeable changes due to the minimal distortion of thestego pixels

6 Conclusion and Future Work

We presented an efficient steganography method for con-cealing a secret image within a cover image based on chaoticmaps and the PSO algorithm)e proposed CPSO algorithmis utilized to determine the best pixel locations in the coverimage in order to embed the secret bits with minimal dis-tortion In the proposed method we divide the cover andsecret images into four blocks to improve the embeddingcapacity )e experimental results show that the proposedmethods perform better than existing schemes in terms ofPSNR and SSIM As a future work we plan to study othertypes of chaotic maps and investigate their effect on pixelselection in order to further enhance the efficiency of theCPSO algorithm

Data Availability

)e data are available on request to the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] B B Zaidan A A Zaidan and M L Mat Kiah ldquoImpact ofdata privacy and confidentiality on developing telemedicineapplications a review participates opinion and expert con-cernsrdquo International Journal of Pharmacology vol 7 no 3pp 382ndash387 2011

[2] A K Singh J Singh and H V Singh ldquoSteganography inimages using lsb techniquerdquo International Journal of LatestTrends in Engineering and Technology (IJLTET) vol 5 no 1pp 426ndash430 2015

[3] A A Zaidan B B Zaidan A K Al-Fraja and H A JalabldquoInvestigate the capability of applying hidden data in text filean overviewrdquo Journal of Applied Sciences vol 10 no 17pp 1916ndash1922 2010

[4] M Hussain A W A Wahab Y I B Idris A T S Ho andK-H Jung ldquoImage steganography in spatial domain a

surveyrdquo Signal Processing Image Communication vol 65pp 46ndash66 2018

[5] B B Zaidan A A Zaidan A K Al-Frajat and H A JalabldquoOn the differences between hiding information and cryp-tography techniques an overviewrdquo Journal of Applied Sci-ences vol 10 no 15 pp 1650ndash1655 2010

[6] I J Kadhim P Premaratne P J Vial and B HalloranldquoComprehensive survey of image steganography techniquesevaluations and trends in future researchrdquo Neurocomputingvol 335 pp 299ndash326 2019

[7] S Kaur A Kaur and K Singh ldquoA survey of image steg-anographyrdquo IJRECE (International Journal of Research inElectronics and Computer Engineering) vol 2 no 3pp 102ndash105 2014

[8] D Wang D Tan and L Liu ldquoParticle swarm optimizationalgorithm an overviewrdquo Soft Computing vol 22 no 2pp 387ndash408 2018

[9] D Tian ldquoParticle swarm optimization with chaos-basedinitialization for numerical optimizationrdquo Intelligent Auto-mation amp Soft Computing vol 24 no 2 pp 331ndash342 2018

[10] A H Mohsin A Zaidan B Zaidan et al ldquoNew method ofimage steganography based on particle swarm optimizationalgorithm in spatial domain for high embedding capacityrdquoIEEE Access vol 7 pp 168994ndash169010 2019

[11] P K Muhuri Z Ashraf and S Goel ldquoA novel image steg-anographic method based on integer wavelet transformationand particle swarm optimizationrdquo Applied Soft Computingvol 92 Article ID 106257 2020

[12] H A Hefny and S S Azab ldquoChaotic particle swarm opti-mizationrdquo in Proceedings of the 2010 7th InternationalConference on Informatics and Systems (INFOS) pp 1ndash8Cairo Egypt March 2010

[13] Z F Yaseen and A A Kareem ldquoImage steganography basedon hybrid edge detector to hide encrypted image using ver-nam algorithmrdquo in Proceedings of the 2019 2nd ScientificConference of Computer Sciences (SCCS) pp 75ndash80 IEEEBaghdad Iraq March 2019

[14] Z Li and Y He ldquoSteganography with pixel-value differencingand modulus function based on psordquo Journal of InformationSecurity and Applications vol 43 pp 47ndash52 2018

[15] A K Sahu and G Swain ldquoPixel overlapping image steg-anography using pvd and modulus functionrdquo 3D Researchvol 9 no 3 p 40 2018

[16] Z Qu Z Li G Xu S Wu and X Wang ldquoQuantum imagesteganography protocol based on quantum image expansionand grover search algorithmrdquo IEEE Access vol 7pp 50849ndash50857 2019

[17] P D Shah and R S Bichkar ldquoA secure spatial domain imagesteganography using genetic algorithm and linear con-gruential generatorrdquo in International Conference on Intelli-gent Computing and Applications pp 119ndash129 SpringerBerlin Germany 2018

[18] R Wazirali W Alasmary M M Mahmoud and A AlhindildquoAn optimized steganography hiding capacity and imper-ceptibly using genetic algorithmsrdquo IEEE Access vol 7pp 133496ndash133508 2019

[19] S A Nie G Sulong R Ali and A Abel ldquo)e use of leastsignificant bit (lsb) and knight tour algorithm for imagesteganography of cover imagerdquo International Journal ofElectrical and Computer Engineering vol 9 no 6 p 52182019

[20] G Swain ldquoVery high capacity image steganography techniqueusing quotient value differencing and lsb substitutionrdquo

10 Security and Communication Networks

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11

of SSIM is in the range [0 1] where the value of 1 indicatesthe highest quality SSIM can be calculated between twoimage windows x and y of common size as

SSIM 2μxμy + c11113872 1113873 2σxy + c21113872 1113873

μ2x + μ2y + c11113872 1113873 σ2x + σ2y + c21113872 1113873 (9)

where μ and σ are themean and variance respectively and c1and c2 are two constants included to avoid instability

In the experiments secret images with various resolu-tions are embedded into the cover image Figure 3 shows thefive standard images used as cover and secret images Firstthe cover and secret images are divided into four separateblocks )en for each block the CPSO algorithm is applied

Apply CPSO algorithm tofind the best pixel locations

Secret bitsprocessing

Embeddingprocess

Stego-image

Extractingprocess

Secret image

Cover image Secret image

Figure 2 Data embedding and extraction

(1) Divide the cover and secret images into blocks using the X-Side-Length and Y-Side-Length particles(2) For each block convert the secret image into a sequence of bits using the SB-Pole and SB-dire particles(3) if (SB-Pole 1)(4) Complement the secret bits(5) end if(6) if (SB-dire 1)(7) Reverse the direction of the secret bits(8) end if(9) Create a sequence of cover pixels using the Direction X-offset and Y-offset particles(10) Convert the cover pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(11) Apply the CPSO algorithm (Algorithm 1)(12) if (number of secret bitslt cover pixel bits)(13) Embed secret bits into corresponding cover pixel bits(14) Embed the particles into the last row of cover image(15) Calculate PSNR of the stego-image(16) end if

ALGORITHM 2 Pseudocode of data embedding

6 Security and Communication Networks

(1) Extract the particles from the last row of stego-image(2) Prepare the image blocks using the X-Side-Length and Y-Side Length particles(3) Create a sequence of stego pixels using the Directions X-offset and Y-offset particles(4) Convert the stego pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(5) if (SB-Pole 1)(6) Complement the secret bits(7) end if(8) if (SB-dire 1)(9) Reverse the direction of the secret bits(10) end if(11) Extract the secret pixels from the secret bits(12) Render secret image using the secret pixels(13) end

ALGORITHM 3 Pseudocode of data extraction

(a) (b)

(c) (d)

Figure 3 Continued

Security and Communication Networks 7

to find the best pixel locations to embed the secret bits suchthat the PSNR of the stego-image is maximized

To study the effect of the secret image size on the quality ofthe stego-image Airplane and Baboon images with differentresolutions are used as secret images and embedded into LenaPeppers and Baboon images of resolution 256times 256 respec-tively Table 4 shows the embedding capacity for different secretimage sizes and the corresponding PSNR of the cover imagesAs shown the PSNR is decreasedwhen the embedding capacity

increases however the corresponding PSNR values are stillacceptable For example when the embedding capacity is204800 bits the PSNR values for Lena are 56145dB and601 dB respectively which are still high

)e performance of the proposed method is comparedwith two related schemes the PSO-based method in [10] andthe PSO-LSB-basedmethod in [11] Table 5 shows the resultsof embedding Lena image with resolution 256times 256 intodifferent cover images (Baboon Lena Airplane and Boat) of

Table 4 PSNR for Lena Peppers and Boat using Airplane and Baboon as secret images

Secret imageCover image Lena Peppers Boat

Resolution Capacity (bits) PSNR (dB) PSNR (dB) PSNR (dB)

Airplane

64times 64 32768 6851 6499 688480times 80 51200 67184 6316 672290times 90 64800 65823 632 661396times 96 73728 6258 6266 6563116times116 107648 6003 5930 6390128times128 131072 5942 5880 6300160times160 204800 5615 5658 6102

Baboon

64times 64 32768 6902 6894 692080times 80 51200 6737 6712 673890times 90 64800 6627 6630 662496times 96 73728 6566 6582 6569116times116 107648 6403 6102 6408128times128 131072 6015 602 6308160times160 204800 6010 5838 6120

Table 5 Comparison between CPSO PSO and PSO-LSB-based methods

Stego-imagePSNR SSIM

PSO-LSB [11] PSO [10] CPSO PSO-LSB [11] PSO [10] CPSOBaboon 511487 5784 63253 09987 09990 09998Lena 511354 5765 63029 09982 09988 09996Airplane 511448 5569 63195 09955 09971 09995Boat 511413 5589 63109 09963 09965 09973

(e)

Figure 3 Standard test images used for evaluation (a) Lena (b) Baboon (c) Peppers (d) Boat and (e) Airplane

8 Security and Communication Networks

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(a)

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(b)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(c)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(d)

3000

2000

1000

0

3000

2000

1000

0

0 50 100 150 200 250 0 50 100 150 200 250

(e)

Figure 4 Cover and stego-images with their respective histograms (a) Lena (b) Baboon (c) Boat (d) Airplane and (e) Peppers

Security and Communication Networks 9

resolution 512times 512 For each method the PSNR and SSIMof the stego-images are calculated )e results show thesuperiority of the proposed CPSO-based method over theother methods in terms of both PSNR and SSIM For ex-ample for the Lana image the CPSO has PSNR 63029 dBwhile the PSO-LSB and PSO have 511354 dB and 5765 dBrespectively In terms of SSIM the CPSO has SSIM 09996while the PSO-LSB and PSO have 09982 and 09988respectively

To further demonstrate the efficiency of the proposedmethod histogram analysis is performed on the stego-images in Table 5 Figure 4 shows the histograms of thecover and stego-images for Lena Baboon Airplane andBoat As shown the cover and stego-images are visuallyalike and the histograms of the stego-images show nonoticeable changes due to the minimal distortion of thestego pixels

6 Conclusion and Future Work

We presented an efficient steganography method for con-cealing a secret image within a cover image based on chaoticmaps and the PSO algorithm)e proposed CPSO algorithmis utilized to determine the best pixel locations in the coverimage in order to embed the secret bits with minimal dis-tortion In the proposed method we divide the cover andsecret images into four blocks to improve the embeddingcapacity )e experimental results show that the proposedmethods perform better than existing schemes in terms ofPSNR and SSIM As a future work we plan to study othertypes of chaotic maps and investigate their effect on pixelselection in order to further enhance the efficiency of theCPSO algorithm

Data Availability

)e data are available on request to the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] B B Zaidan A A Zaidan and M L Mat Kiah ldquoImpact ofdata privacy and confidentiality on developing telemedicineapplications a review participates opinion and expert con-cernsrdquo International Journal of Pharmacology vol 7 no 3pp 382ndash387 2011

[2] A K Singh J Singh and H V Singh ldquoSteganography inimages using lsb techniquerdquo International Journal of LatestTrends in Engineering and Technology (IJLTET) vol 5 no 1pp 426ndash430 2015

[3] A A Zaidan B B Zaidan A K Al-Fraja and H A JalabldquoInvestigate the capability of applying hidden data in text filean overviewrdquo Journal of Applied Sciences vol 10 no 17pp 1916ndash1922 2010

[4] M Hussain A W A Wahab Y I B Idris A T S Ho andK-H Jung ldquoImage steganography in spatial domain a

surveyrdquo Signal Processing Image Communication vol 65pp 46ndash66 2018

[5] B B Zaidan A A Zaidan A K Al-Frajat and H A JalabldquoOn the differences between hiding information and cryp-tography techniques an overviewrdquo Journal of Applied Sci-ences vol 10 no 15 pp 1650ndash1655 2010

[6] I J Kadhim P Premaratne P J Vial and B HalloranldquoComprehensive survey of image steganography techniquesevaluations and trends in future researchrdquo Neurocomputingvol 335 pp 299ndash326 2019

[7] S Kaur A Kaur and K Singh ldquoA survey of image steg-anographyrdquo IJRECE (International Journal of Research inElectronics and Computer Engineering) vol 2 no 3pp 102ndash105 2014

[8] D Wang D Tan and L Liu ldquoParticle swarm optimizationalgorithm an overviewrdquo Soft Computing vol 22 no 2pp 387ndash408 2018

[9] D Tian ldquoParticle swarm optimization with chaos-basedinitialization for numerical optimizationrdquo Intelligent Auto-mation amp Soft Computing vol 24 no 2 pp 331ndash342 2018

[10] A H Mohsin A Zaidan B Zaidan et al ldquoNew method ofimage steganography based on particle swarm optimizationalgorithm in spatial domain for high embedding capacityrdquoIEEE Access vol 7 pp 168994ndash169010 2019

[11] P K Muhuri Z Ashraf and S Goel ldquoA novel image steg-anographic method based on integer wavelet transformationand particle swarm optimizationrdquo Applied Soft Computingvol 92 Article ID 106257 2020

[12] H A Hefny and S S Azab ldquoChaotic particle swarm opti-mizationrdquo in Proceedings of the 2010 7th InternationalConference on Informatics and Systems (INFOS) pp 1ndash8Cairo Egypt March 2010

[13] Z F Yaseen and A A Kareem ldquoImage steganography basedon hybrid edge detector to hide encrypted image using ver-nam algorithmrdquo in Proceedings of the 2019 2nd ScientificConference of Computer Sciences (SCCS) pp 75ndash80 IEEEBaghdad Iraq March 2019

[14] Z Li and Y He ldquoSteganography with pixel-value differencingand modulus function based on psordquo Journal of InformationSecurity and Applications vol 43 pp 47ndash52 2018

[15] A K Sahu and G Swain ldquoPixel overlapping image steg-anography using pvd and modulus functionrdquo 3D Researchvol 9 no 3 p 40 2018

[16] Z Qu Z Li G Xu S Wu and X Wang ldquoQuantum imagesteganography protocol based on quantum image expansionand grover search algorithmrdquo IEEE Access vol 7pp 50849ndash50857 2019

[17] P D Shah and R S Bichkar ldquoA secure spatial domain imagesteganography using genetic algorithm and linear con-gruential generatorrdquo in International Conference on Intelli-gent Computing and Applications pp 119ndash129 SpringerBerlin Germany 2018

[18] R Wazirali W Alasmary M M Mahmoud and A AlhindildquoAn optimized steganography hiding capacity and imper-ceptibly using genetic algorithmsrdquo IEEE Access vol 7pp 133496ndash133508 2019

[19] S A Nie G Sulong R Ali and A Abel ldquo)e use of leastsignificant bit (lsb) and knight tour algorithm for imagesteganography of cover imagerdquo International Journal ofElectrical and Computer Engineering vol 9 no 6 p 52182019

[20] G Swain ldquoVery high capacity image steganography techniqueusing quotient value differencing and lsb substitutionrdquo

10 Security and Communication Networks

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11

(1) Extract the particles from the last row of stego-image(2) Prepare the image blocks using the X-Side-Length and Y-Side Length particles(3) Create a sequence of stego pixels using the Directions X-offset and Y-offset particles(4) Convert the stego pixels sequence into a sequence of bits using the Bit-Planes and BB-dire particles(5) if (SB-Pole 1)(6) Complement the secret bits(7) end if(8) if (SB-dire 1)(9) Reverse the direction of the secret bits(10) end if(11) Extract the secret pixels from the secret bits(12) Render secret image using the secret pixels(13) end

ALGORITHM 3 Pseudocode of data extraction

(a) (b)

(c) (d)

Figure 3 Continued

Security and Communication Networks 7

to find the best pixel locations to embed the secret bits suchthat the PSNR of the stego-image is maximized

To study the effect of the secret image size on the quality ofthe stego-image Airplane and Baboon images with differentresolutions are used as secret images and embedded into LenaPeppers and Baboon images of resolution 256times 256 respec-tively Table 4 shows the embedding capacity for different secretimage sizes and the corresponding PSNR of the cover imagesAs shown the PSNR is decreasedwhen the embedding capacity

increases however the corresponding PSNR values are stillacceptable For example when the embedding capacity is204800 bits the PSNR values for Lena are 56145dB and601 dB respectively which are still high

)e performance of the proposed method is comparedwith two related schemes the PSO-based method in [10] andthe PSO-LSB-basedmethod in [11] Table 5 shows the resultsof embedding Lena image with resolution 256times 256 intodifferent cover images (Baboon Lena Airplane and Boat) of

Table 4 PSNR for Lena Peppers and Boat using Airplane and Baboon as secret images

Secret imageCover image Lena Peppers Boat

Resolution Capacity (bits) PSNR (dB) PSNR (dB) PSNR (dB)

Airplane

64times 64 32768 6851 6499 688480times 80 51200 67184 6316 672290times 90 64800 65823 632 661396times 96 73728 6258 6266 6563116times116 107648 6003 5930 6390128times128 131072 5942 5880 6300160times160 204800 5615 5658 6102

Baboon

64times 64 32768 6902 6894 692080times 80 51200 6737 6712 673890times 90 64800 6627 6630 662496times 96 73728 6566 6582 6569116times116 107648 6403 6102 6408128times128 131072 6015 602 6308160times160 204800 6010 5838 6120

Table 5 Comparison between CPSO PSO and PSO-LSB-based methods

Stego-imagePSNR SSIM

PSO-LSB [11] PSO [10] CPSO PSO-LSB [11] PSO [10] CPSOBaboon 511487 5784 63253 09987 09990 09998Lena 511354 5765 63029 09982 09988 09996Airplane 511448 5569 63195 09955 09971 09995Boat 511413 5589 63109 09963 09965 09973

(e)

Figure 3 Standard test images used for evaluation (a) Lena (b) Baboon (c) Peppers (d) Boat and (e) Airplane

8 Security and Communication Networks

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(a)

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(b)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(c)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(d)

3000

2000

1000

0

3000

2000

1000

0

0 50 100 150 200 250 0 50 100 150 200 250

(e)

Figure 4 Cover and stego-images with their respective histograms (a) Lena (b) Baboon (c) Boat (d) Airplane and (e) Peppers

Security and Communication Networks 9

resolution 512times 512 For each method the PSNR and SSIMof the stego-images are calculated )e results show thesuperiority of the proposed CPSO-based method over theother methods in terms of both PSNR and SSIM For ex-ample for the Lana image the CPSO has PSNR 63029 dBwhile the PSO-LSB and PSO have 511354 dB and 5765 dBrespectively In terms of SSIM the CPSO has SSIM 09996while the PSO-LSB and PSO have 09982 and 09988respectively

To further demonstrate the efficiency of the proposedmethod histogram analysis is performed on the stego-images in Table 5 Figure 4 shows the histograms of thecover and stego-images for Lena Baboon Airplane andBoat As shown the cover and stego-images are visuallyalike and the histograms of the stego-images show nonoticeable changes due to the minimal distortion of thestego pixels

6 Conclusion and Future Work

We presented an efficient steganography method for con-cealing a secret image within a cover image based on chaoticmaps and the PSO algorithm)e proposed CPSO algorithmis utilized to determine the best pixel locations in the coverimage in order to embed the secret bits with minimal dis-tortion In the proposed method we divide the cover andsecret images into four blocks to improve the embeddingcapacity )e experimental results show that the proposedmethods perform better than existing schemes in terms ofPSNR and SSIM As a future work we plan to study othertypes of chaotic maps and investigate their effect on pixelselection in order to further enhance the efficiency of theCPSO algorithm

Data Availability

)e data are available on request to the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] B B Zaidan A A Zaidan and M L Mat Kiah ldquoImpact ofdata privacy and confidentiality on developing telemedicineapplications a review participates opinion and expert con-cernsrdquo International Journal of Pharmacology vol 7 no 3pp 382ndash387 2011

[2] A K Singh J Singh and H V Singh ldquoSteganography inimages using lsb techniquerdquo International Journal of LatestTrends in Engineering and Technology (IJLTET) vol 5 no 1pp 426ndash430 2015

[3] A A Zaidan B B Zaidan A K Al-Fraja and H A JalabldquoInvestigate the capability of applying hidden data in text filean overviewrdquo Journal of Applied Sciences vol 10 no 17pp 1916ndash1922 2010

[4] M Hussain A W A Wahab Y I B Idris A T S Ho andK-H Jung ldquoImage steganography in spatial domain a

surveyrdquo Signal Processing Image Communication vol 65pp 46ndash66 2018

[5] B B Zaidan A A Zaidan A K Al-Frajat and H A JalabldquoOn the differences between hiding information and cryp-tography techniques an overviewrdquo Journal of Applied Sci-ences vol 10 no 15 pp 1650ndash1655 2010

[6] I J Kadhim P Premaratne P J Vial and B HalloranldquoComprehensive survey of image steganography techniquesevaluations and trends in future researchrdquo Neurocomputingvol 335 pp 299ndash326 2019

[7] S Kaur A Kaur and K Singh ldquoA survey of image steg-anographyrdquo IJRECE (International Journal of Research inElectronics and Computer Engineering) vol 2 no 3pp 102ndash105 2014

[8] D Wang D Tan and L Liu ldquoParticle swarm optimizationalgorithm an overviewrdquo Soft Computing vol 22 no 2pp 387ndash408 2018

[9] D Tian ldquoParticle swarm optimization with chaos-basedinitialization for numerical optimizationrdquo Intelligent Auto-mation amp Soft Computing vol 24 no 2 pp 331ndash342 2018

[10] A H Mohsin A Zaidan B Zaidan et al ldquoNew method ofimage steganography based on particle swarm optimizationalgorithm in spatial domain for high embedding capacityrdquoIEEE Access vol 7 pp 168994ndash169010 2019

[11] P K Muhuri Z Ashraf and S Goel ldquoA novel image steg-anographic method based on integer wavelet transformationand particle swarm optimizationrdquo Applied Soft Computingvol 92 Article ID 106257 2020

[12] H A Hefny and S S Azab ldquoChaotic particle swarm opti-mizationrdquo in Proceedings of the 2010 7th InternationalConference on Informatics and Systems (INFOS) pp 1ndash8Cairo Egypt March 2010

[13] Z F Yaseen and A A Kareem ldquoImage steganography basedon hybrid edge detector to hide encrypted image using ver-nam algorithmrdquo in Proceedings of the 2019 2nd ScientificConference of Computer Sciences (SCCS) pp 75ndash80 IEEEBaghdad Iraq March 2019

[14] Z Li and Y He ldquoSteganography with pixel-value differencingand modulus function based on psordquo Journal of InformationSecurity and Applications vol 43 pp 47ndash52 2018

[15] A K Sahu and G Swain ldquoPixel overlapping image steg-anography using pvd and modulus functionrdquo 3D Researchvol 9 no 3 p 40 2018

[16] Z Qu Z Li G Xu S Wu and X Wang ldquoQuantum imagesteganography protocol based on quantum image expansionand grover search algorithmrdquo IEEE Access vol 7pp 50849ndash50857 2019

[17] P D Shah and R S Bichkar ldquoA secure spatial domain imagesteganography using genetic algorithm and linear con-gruential generatorrdquo in International Conference on Intelli-gent Computing and Applications pp 119ndash129 SpringerBerlin Germany 2018

[18] R Wazirali W Alasmary M M Mahmoud and A AlhindildquoAn optimized steganography hiding capacity and imper-ceptibly using genetic algorithmsrdquo IEEE Access vol 7pp 133496ndash133508 2019

[19] S A Nie G Sulong R Ali and A Abel ldquo)e use of leastsignificant bit (lsb) and knight tour algorithm for imagesteganography of cover imagerdquo International Journal ofElectrical and Computer Engineering vol 9 no 6 p 52182019

[20] G Swain ldquoVery high capacity image steganography techniqueusing quotient value differencing and lsb substitutionrdquo

10 Security and Communication Networks

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11

to find the best pixel locations to embed the secret bits suchthat the PSNR of the stego-image is maximized

To study the effect of the secret image size on the quality ofthe stego-image Airplane and Baboon images with differentresolutions are used as secret images and embedded into LenaPeppers and Baboon images of resolution 256times 256 respec-tively Table 4 shows the embedding capacity for different secretimage sizes and the corresponding PSNR of the cover imagesAs shown the PSNR is decreasedwhen the embedding capacity

increases however the corresponding PSNR values are stillacceptable For example when the embedding capacity is204800 bits the PSNR values for Lena are 56145dB and601 dB respectively which are still high

)e performance of the proposed method is comparedwith two related schemes the PSO-based method in [10] andthe PSO-LSB-basedmethod in [11] Table 5 shows the resultsof embedding Lena image with resolution 256times 256 intodifferent cover images (Baboon Lena Airplane and Boat) of

Table 4 PSNR for Lena Peppers and Boat using Airplane and Baboon as secret images

Secret imageCover image Lena Peppers Boat

Resolution Capacity (bits) PSNR (dB) PSNR (dB) PSNR (dB)

Airplane

64times 64 32768 6851 6499 688480times 80 51200 67184 6316 672290times 90 64800 65823 632 661396times 96 73728 6258 6266 6563116times116 107648 6003 5930 6390128times128 131072 5942 5880 6300160times160 204800 5615 5658 6102

Baboon

64times 64 32768 6902 6894 692080times 80 51200 6737 6712 673890times 90 64800 6627 6630 662496times 96 73728 6566 6582 6569116times116 107648 6403 6102 6408128times128 131072 6015 602 6308160times160 204800 6010 5838 6120

Table 5 Comparison between CPSO PSO and PSO-LSB-based methods

Stego-imagePSNR SSIM

PSO-LSB [11] PSO [10] CPSO PSO-LSB [11] PSO [10] CPSOBaboon 511487 5784 63253 09987 09990 09998Lena 511354 5765 63029 09982 09988 09996Airplane 511448 5569 63195 09955 09971 09995Boat 511413 5589 63109 09963 09965 09973

(e)

Figure 3 Standard test images used for evaluation (a) Lena (b) Baboon (c) Peppers (d) Boat and (e) Airplane

8 Security and Communication Networks

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(a)

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(b)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(c)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(d)

3000

2000

1000

0

3000

2000

1000

0

0 50 100 150 200 250 0 50 100 150 200 250

(e)

Figure 4 Cover and stego-images with their respective histograms (a) Lena (b) Baboon (c) Boat (d) Airplane and (e) Peppers

Security and Communication Networks 9

resolution 512times 512 For each method the PSNR and SSIMof the stego-images are calculated )e results show thesuperiority of the proposed CPSO-based method over theother methods in terms of both PSNR and SSIM For ex-ample for the Lana image the CPSO has PSNR 63029 dBwhile the PSO-LSB and PSO have 511354 dB and 5765 dBrespectively In terms of SSIM the CPSO has SSIM 09996while the PSO-LSB and PSO have 09982 and 09988respectively

To further demonstrate the efficiency of the proposedmethod histogram analysis is performed on the stego-images in Table 5 Figure 4 shows the histograms of thecover and stego-images for Lena Baboon Airplane andBoat As shown the cover and stego-images are visuallyalike and the histograms of the stego-images show nonoticeable changes due to the minimal distortion of thestego pixels

6 Conclusion and Future Work

We presented an efficient steganography method for con-cealing a secret image within a cover image based on chaoticmaps and the PSO algorithm)e proposed CPSO algorithmis utilized to determine the best pixel locations in the coverimage in order to embed the secret bits with minimal dis-tortion In the proposed method we divide the cover andsecret images into four blocks to improve the embeddingcapacity )e experimental results show that the proposedmethods perform better than existing schemes in terms ofPSNR and SSIM As a future work we plan to study othertypes of chaotic maps and investigate their effect on pixelselection in order to further enhance the efficiency of theCPSO algorithm

Data Availability

)e data are available on request to the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] B B Zaidan A A Zaidan and M L Mat Kiah ldquoImpact ofdata privacy and confidentiality on developing telemedicineapplications a review participates opinion and expert con-cernsrdquo International Journal of Pharmacology vol 7 no 3pp 382ndash387 2011

[2] A K Singh J Singh and H V Singh ldquoSteganography inimages using lsb techniquerdquo International Journal of LatestTrends in Engineering and Technology (IJLTET) vol 5 no 1pp 426ndash430 2015

[3] A A Zaidan B B Zaidan A K Al-Fraja and H A JalabldquoInvestigate the capability of applying hidden data in text filean overviewrdquo Journal of Applied Sciences vol 10 no 17pp 1916ndash1922 2010

[4] M Hussain A W A Wahab Y I B Idris A T S Ho andK-H Jung ldquoImage steganography in spatial domain a

surveyrdquo Signal Processing Image Communication vol 65pp 46ndash66 2018

[5] B B Zaidan A A Zaidan A K Al-Frajat and H A JalabldquoOn the differences between hiding information and cryp-tography techniques an overviewrdquo Journal of Applied Sci-ences vol 10 no 15 pp 1650ndash1655 2010

[6] I J Kadhim P Premaratne P J Vial and B HalloranldquoComprehensive survey of image steganography techniquesevaluations and trends in future researchrdquo Neurocomputingvol 335 pp 299ndash326 2019

[7] S Kaur A Kaur and K Singh ldquoA survey of image steg-anographyrdquo IJRECE (International Journal of Research inElectronics and Computer Engineering) vol 2 no 3pp 102ndash105 2014

[8] D Wang D Tan and L Liu ldquoParticle swarm optimizationalgorithm an overviewrdquo Soft Computing vol 22 no 2pp 387ndash408 2018

[9] D Tian ldquoParticle swarm optimization with chaos-basedinitialization for numerical optimizationrdquo Intelligent Auto-mation amp Soft Computing vol 24 no 2 pp 331ndash342 2018

[10] A H Mohsin A Zaidan B Zaidan et al ldquoNew method ofimage steganography based on particle swarm optimizationalgorithm in spatial domain for high embedding capacityrdquoIEEE Access vol 7 pp 168994ndash169010 2019

[11] P K Muhuri Z Ashraf and S Goel ldquoA novel image steg-anographic method based on integer wavelet transformationand particle swarm optimizationrdquo Applied Soft Computingvol 92 Article ID 106257 2020

[12] H A Hefny and S S Azab ldquoChaotic particle swarm opti-mizationrdquo in Proceedings of the 2010 7th InternationalConference on Informatics and Systems (INFOS) pp 1ndash8Cairo Egypt March 2010

[13] Z F Yaseen and A A Kareem ldquoImage steganography basedon hybrid edge detector to hide encrypted image using ver-nam algorithmrdquo in Proceedings of the 2019 2nd ScientificConference of Computer Sciences (SCCS) pp 75ndash80 IEEEBaghdad Iraq March 2019

[14] Z Li and Y He ldquoSteganography with pixel-value differencingand modulus function based on psordquo Journal of InformationSecurity and Applications vol 43 pp 47ndash52 2018

[15] A K Sahu and G Swain ldquoPixel overlapping image steg-anography using pvd and modulus functionrdquo 3D Researchvol 9 no 3 p 40 2018

[16] Z Qu Z Li G Xu S Wu and X Wang ldquoQuantum imagesteganography protocol based on quantum image expansionand grover search algorithmrdquo IEEE Access vol 7pp 50849ndash50857 2019

[17] P D Shah and R S Bichkar ldquoA secure spatial domain imagesteganography using genetic algorithm and linear con-gruential generatorrdquo in International Conference on Intelli-gent Computing and Applications pp 119ndash129 SpringerBerlin Germany 2018

[18] R Wazirali W Alasmary M M Mahmoud and A AlhindildquoAn optimized steganography hiding capacity and imper-ceptibly using genetic algorithmsrdquo IEEE Access vol 7pp 133496ndash133508 2019

[19] S A Nie G Sulong R Ali and A Abel ldquo)e use of leastsignificant bit (lsb) and knight tour algorithm for imagesteganography of cover imagerdquo International Journal ofElectrical and Computer Engineering vol 9 no 6 p 52182019

[20] G Swain ldquoVery high capacity image steganography techniqueusing quotient value differencing and lsb substitutionrdquo

10 Security and Communication Networks

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(a)

3000

2000

1000

0

0 50 100 150 200 250

3000

2000

1000

0

0 50 100 150 200 250

(b)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(c)

3000

4000

2000

1000

0

0 50 100 150 200 250

3000

4000

2000

1000

0

0 50 100 150 200 250

(d)

3000

2000

1000

0

3000

2000

1000

0

0 50 100 150 200 250 0 50 100 150 200 250

(e)

Figure 4 Cover and stego-images with their respective histograms (a) Lena (b) Baboon (c) Boat (d) Airplane and (e) Peppers

Security and Communication Networks 9

resolution 512times 512 For each method the PSNR and SSIMof the stego-images are calculated )e results show thesuperiority of the proposed CPSO-based method over theother methods in terms of both PSNR and SSIM For ex-ample for the Lana image the CPSO has PSNR 63029 dBwhile the PSO-LSB and PSO have 511354 dB and 5765 dBrespectively In terms of SSIM the CPSO has SSIM 09996while the PSO-LSB and PSO have 09982 and 09988respectively

To further demonstrate the efficiency of the proposedmethod histogram analysis is performed on the stego-images in Table 5 Figure 4 shows the histograms of thecover and stego-images for Lena Baboon Airplane andBoat As shown the cover and stego-images are visuallyalike and the histograms of the stego-images show nonoticeable changes due to the minimal distortion of thestego pixels

6 Conclusion and Future Work

We presented an efficient steganography method for con-cealing a secret image within a cover image based on chaoticmaps and the PSO algorithm)e proposed CPSO algorithmis utilized to determine the best pixel locations in the coverimage in order to embed the secret bits with minimal dis-tortion In the proposed method we divide the cover andsecret images into four blocks to improve the embeddingcapacity )e experimental results show that the proposedmethods perform better than existing schemes in terms ofPSNR and SSIM As a future work we plan to study othertypes of chaotic maps and investigate their effect on pixelselection in order to further enhance the efficiency of theCPSO algorithm

Data Availability

)e data are available on request to the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] B B Zaidan A A Zaidan and M L Mat Kiah ldquoImpact ofdata privacy and confidentiality on developing telemedicineapplications a review participates opinion and expert con-cernsrdquo International Journal of Pharmacology vol 7 no 3pp 382ndash387 2011

[2] A K Singh J Singh and H V Singh ldquoSteganography inimages using lsb techniquerdquo International Journal of LatestTrends in Engineering and Technology (IJLTET) vol 5 no 1pp 426ndash430 2015

[3] A A Zaidan B B Zaidan A K Al-Fraja and H A JalabldquoInvestigate the capability of applying hidden data in text filean overviewrdquo Journal of Applied Sciences vol 10 no 17pp 1916ndash1922 2010

[4] M Hussain A W A Wahab Y I B Idris A T S Ho andK-H Jung ldquoImage steganography in spatial domain a

surveyrdquo Signal Processing Image Communication vol 65pp 46ndash66 2018

[5] B B Zaidan A A Zaidan A K Al-Frajat and H A JalabldquoOn the differences between hiding information and cryp-tography techniques an overviewrdquo Journal of Applied Sci-ences vol 10 no 15 pp 1650ndash1655 2010

[6] I J Kadhim P Premaratne P J Vial and B HalloranldquoComprehensive survey of image steganography techniquesevaluations and trends in future researchrdquo Neurocomputingvol 335 pp 299ndash326 2019

[7] S Kaur A Kaur and K Singh ldquoA survey of image steg-anographyrdquo IJRECE (International Journal of Research inElectronics and Computer Engineering) vol 2 no 3pp 102ndash105 2014

[8] D Wang D Tan and L Liu ldquoParticle swarm optimizationalgorithm an overviewrdquo Soft Computing vol 22 no 2pp 387ndash408 2018

[9] D Tian ldquoParticle swarm optimization with chaos-basedinitialization for numerical optimizationrdquo Intelligent Auto-mation amp Soft Computing vol 24 no 2 pp 331ndash342 2018

[10] A H Mohsin A Zaidan B Zaidan et al ldquoNew method ofimage steganography based on particle swarm optimizationalgorithm in spatial domain for high embedding capacityrdquoIEEE Access vol 7 pp 168994ndash169010 2019

[11] P K Muhuri Z Ashraf and S Goel ldquoA novel image steg-anographic method based on integer wavelet transformationand particle swarm optimizationrdquo Applied Soft Computingvol 92 Article ID 106257 2020

[12] H A Hefny and S S Azab ldquoChaotic particle swarm opti-mizationrdquo in Proceedings of the 2010 7th InternationalConference on Informatics and Systems (INFOS) pp 1ndash8Cairo Egypt March 2010

[13] Z F Yaseen and A A Kareem ldquoImage steganography basedon hybrid edge detector to hide encrypted image using ver-nam algorithmrdquo in Proceedings of the 2019 2nd ScientificConference of Computer Sciences (SCCS) pp 75ndash80 IEEEBaghdad Iraq March 2019

[14] Z Li and Y He ldquoSteganography with pixel-value differencingand modulus function based on psordquo Journal of InformationSecurity and Applications vol 43 pp 47ndash52 2018

[15] A K Sahu and G Swain ldquoPixel overlapping image steg-anography using pvd and modulus functionrdquo 3D Researchvol 9 no 3 p 40 2018

[16] Z Qu Z Li G Xu S Wu and X Wang ldquoQuantum imagesteganography protocol based on quantum image expansionand grover search algorithmrdquo IEEE Access vol 7pp 50849ndash50857 2019

[17] P D Shah and R S Bichkar ldquoA secure spatial domain imagesteganography using genetic algorithm and linear con-gruential generatorrdquo in International Conference on Intelli-gent Computing and Applications pp 119ndash129 SpringerBerlin Germany 2018

[18] R Wazirali W Alasmary M M Mahmoud and A AlhindildquoAn optimized steganography hiding capacity and imper-ceptibly using genetic algorithmsrdquo IEEE Access vol 7pp 133496ndash133508 2019

[19] S A Nie G Sulong R Ali and A Abel ldquo)e use of leastsignificant bit (lsb) and knight tour algorithm for imagesteganography of cover imagerdquo International Journal ofElectrical and Computer Engineering vol 9 no 6 p 52182019

[20] G Swain ldquoVery high capacity image steganography techniqueusing quotient value differencing and lsb substitutionrdquo

10 Security and Communication Networks

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11

resolution 512times 512 For each method the PSNR and SSIMof the stego-images are calculated )e results show thesuperiority of the proposed CPSO-based method over theother methods in terms of both PSNR and SSIM For ex-ample for the Lana image the CPSO has PSNR 63029 dBwhile the PSO-LSB and PSO have 511354 dB and 5765 dBrespectively In terms of SSIM the CPSO has SSIM 09996while the PSO-LSB and PSO have 09982 and 09988respectively

To further demonstrate the efficiency of the proposedmethod histogram analysis is performed on the stego-images in Table 5 Figure 4 shows the histograms of thecover and stego-images for Lena Baboon Airplane andBoat As shown the cover and stego-images are visuallyalike and the histograms of the stego-images show nonoticeable changes due to the minimal distortion of thestego pixels

6 Conclusion and Future Work

We presented an efficient steganography method for con-cealing a secret image within a cover image based on chaoticmaps and the PSO algorithm)e proposed CPSO algorithmis utilized to determine the best pixel locations in the coverimage in order to embed the secret bits with minimal dis-tortion In the proposed method we divide the cover andsecret images into four blocks to improve the embeddingcapacity )e experimental results show that the proposedmethods perform better than existing schemes in terms ofPSNR and SSIM As a future work we plan to study othertypes of chaotic maps and investigate their effect on pixelselection in order to further enhance the efficiency of theCPSO algorithm

Data Availability

)e data are available on request to the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] B B Zaidan A A Zaidan and M L Mat Kiah ldquoImpact ofdata privacy and confidentiality on developing telemedicineapplications a review participates opinion and expert con-cernsrdquo International Journal of Pharmacology vol 7 no 3pp 382ndash387 2011

[2] A K Singh J Singh and H V Singh ldquoSteganography inimages using lsb techniquerdquo International Journal of LatestTrends in Engineering and Technology (IJLTET) vol 5 no 1pp 426ndash430 2015

[3] A A Zaidan B B Zaidan A K Al-Fraja and H A JalabldquoInvestigate the capability of applying hidden data in text filean overviewrdquo Journal of Applied Sciences vol 10 no 17pp 1916ndash1922 2010

[4] M Hussain A W A Wahab Y I B Idris A T S Ho andK-H Jung ldquoImage steganography in spatial domain a

surveyrdquo Signal Processing Image Communication vol 65pp 46ndash66 2018

[5] B B Zaidan A A Zaidan A K Al-Frajat and H A JalabldquoOn the differences between hiding information and cryp-tography techniques an overviewrdquo Journal of Applied Sci-ences vol 10 no 15 pp 1650ndash1655 2010

[6] I J Kadhim P Premaratne P J Vial and B HalloranldquoComprehensive survey of image steganography techniquesevaluations and trends in future researchrdquo Neurocomputingvol 335 pp 299ndash326 2019

[7] S Kaur A Kaur and K Singh ldquoA survey of image steg-anographyrdquo IJRECE (International Journal of Research inElectronics and Computer Engineering) vol 2 no 3pp 102ndash105 2014

[8] D Wang D Tan and L Liu ldquoParticle swarm optimizationalgorithm an overviewrdquo Soft Computing vol 22 no 2pp 387ndash408 2018

[9] D Tian ldquoParticle swarm optimization with chaos-basedinitialization for numerical optimizationrdquo Intelligent Auto-mation amp Soft Computing vol 24 no 2 pp 331ndash342 2018

[10] A H Mohsin A Zaidan B Zaidan et al ldquoNew method ofimage steganography based on particle swarm optimizationalgorithm in spatial domain for high embedding capacityrdquoIEEE Access vol 7 pp 168994ndash169010 2019

[11] P K Muhuri Z Ashraf and S Goel ldquoA novel image steg-anographic method based on integer wavelet transformationand particle swarm optimizationrdquo Applied Soft Computingvol 92 Article ID 106257 2020

[12] H A Hefny and S S Azab ldquoChaotic particle swarm opti-mizationrdquo in Proceedings of the 2010 7th InternationalConference on Informatics and Systems (INFOS) pp 1ndash8Cairo Egypt March 2010

[13] Z F Yaseen and A A Kareem ldquoImage steganography basedon hybrid edge detector to hide encrypted image using ver-nam algorithmrdquo in Proceedings of the 2019 2nd ScientificConference of Computer Sciences (SCCS) pp 75ndash80 IEEEBaghdad Iraq March 2019

[14] Z Li and Y He ldquoSteganography with pixel-value differencingand modulus function based on psordquo Journal of InformationSecurity and Applications vol 43 pp 47ndash52 2018

[15] A K Sahu and G Swain ldquoPixel overlapping image steg-anography using pvd and modulus functionrdquo 3D Researchvol 9 no 3 p 40 2018

[16] Z Qu Z Li G Xu S Wu and X Wang ldquoQuantum imagesteganography protocol based on quantum image expansionand grover search algorithmrdquo IEEE Access vol 7pp 50849ndash50857 2019

[17] P D Shah and R S Bichkar ldquoA secure spatial domain imagesteganography using genetic algorithm and linear con-gruential generatorrdquo in International Conference on Intelli-gent Computing and Applications pp 119ndash129 SpringerBerlin Germany 2018

[18] R Wazirali W Alasmary M M Mahmoud and A AlhindildquoAn optimized steganography hiding capacity and imper-ceptibly using genetic algorithmsrdquo IEEE Access vol 7pp 133496ndash133508 2019

[19] S A Nie G Sulong R Ali and A Abel ldquo)e use of leastsignificant bit (lsb) and knight tour algorithm for imagesteganography of cover imagerdquo International Journal ofElectrical and Computer Engineering vol 9 no 6 p 52182019

[20] G Swain ldquoVery high capacity image steganography techniqueusing quotient value differencing and lsb substitutionrdquo

10 Security and Communication Networks

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11

Arabian Journal for Science and Engineering vol 44 no 4pp 2995ndash3004 2019

[21] S I Nipanikar V Hima Deepthi and N Kulkarni ldquoA sparserepresentation based image steganography using particleswarm optimization and wavelet transformrdquo AlexandriaEngineering Journal vol 57 no 4 pp 2343ndash2356 2018

[22] S Sajasi and A-M Eftekhari Moghadam ldquoAn adaptive imagesteganographic scheme based on noise visibility function andan optimal chaotic based encryption methodrdquo Applied SoftComputing vol 30 pp 375ndash389 2015

[23] P S Raja and E Baburaj ldquoAn efficient data embeddingscheme for digital images based on particle swarm optimi-zation with lsbmrrdquo in Proceedings of the ird InternationalConference on Computational Intelligence and InformationTechnology (CIIT 2013) Mumbai India October 2013

[24] S Wang B Yang and X Niu ldquoA secure steganographymethod based on genetic algorithmrdquo Journal of InformationHiding and Multimedia Signal Processing vol 1 no 1pp 28ndash35 2010

[25] E Ghasemi and J Shanbehzadeh ldquoAn imperceptible steg-anographic method based on genetic algorithmrdquo in Pro-ceedings of the 2010 5th International Symposium onTelecommunications pp 836ndash839 IEEE Tehran Iran De-cember 2010

[26] T Dongping ldquoParticle swarm optimization with chaotic mapsand Gaussian mutation for function optimizationrdquo Interna-tional Journal of Grid and Distributed Computing vol 8 no 4pp 123ndash134 2015

[27] S Rajendran and M Doraipandian ldquoChaotic map basedrandom image steganography using lsb techniquerdquo Inter-national Journal of Network Security vol 19 no 4 pp 593ndash598 2017

[28] Y Yue L Cao J Hu S Cai B Hang and H Wu ldquoA novelhybrid location algorithm based on chaotic particle swarmoptimization for mobile position estimationrdquo IEEE Accessvol 7 pp 58541ndash58552 2019

[29] A Singh and U Jauhari ldquoA symmetric steganography withsecret sharing and psnr analysis for image steganographyrdquoInternational Journal of Scientific and Engineering Researchvol 3 no 6 pp 1279ndash1285 2012

[30] Z Wang A C Bovik H R Sheikh and E P SimoncellildquoImage quality assessment from error visibility to structuralsimilarityrdquo IEEE Transactions on Image Processing vol 13no 4 pp 600ndash612 2004

Security and Communication Networks 11