Digital Image Steganography Based on Combination of DCT and DWT_springer2010

7/27/2019 Digital Image Steganography Based on Combination of DCT and DWT_springer2010

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V. V Das, R. Vijaykumar et al. (Eds.): ICT 2010, CCIS 101, pp. 596601, 2010.

Springer-Verlag Berlin Heidelberg 2010

Digital Image Steganography Based on Combination of

DCT and DWT

Vijay Kumar1

and Dinesh Kumar2

1 CSE Department, JCDMCOE, Sirsa, Haryana, India

[email protected] CSE Department, GJUS&T, Hisar, Haryana, India

[email protected]

Abstract. In this paper, a copyright protection scheme that combines theDiscrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) is

proposed. The proposed scheme first extracts the DCT coefficients of secret

image by applying DCT. After that, image features are extracted from cover

image and from DCT coefficients by applying DWT on both separately. Hiding

of extracted features of DCT coefficients in the features of cover image is done

using two different secret keys. Experimentation has been done using eight

different attacks. Experimental results demonstrate that combining the two

transforms improves the performance of the steganography technique in terms

of PSNR value and the performance is better as compared to that achieved

using DWT transform only. The extracted image has good visual quality also.

Keywords: Discrete Cosine Transform, Discrete Wavelet Transform,

Steganography.

1 Introduction

The rapid growth of multimedia processing and Internet technologies in recent years

has made it possible to distribute and exchange huge amount of multimedia data moreeasily and quickly than ever at low cost. The data can be easily edited with almost

negligible loss using multimedia processing techniques. Therefore, the need for the

copyright protection of digital data has emerged. Nowadays, Steganography has

become the focus of research for copyright protection.

There are two approaches related to steganography i.e., spatial-domain and

frequency-domain approach [13]. In the former approach, the secret messages are

embedded into least significant pixels of cover images. They are fast but sensitive to

image processing attacks. The latter approach contains transforming the cover image

into the frequency domain coefficients before embedding secret messages in it. Thetransformation can be either Discrete Cosine transform (DCT) or Discrete Wavelet

Transform (DWT) etc. Though these methods are more difficult and slower than

spatial domain, yet they have an advantage of being more secure and noise tolerant

[12]. Among these methods, DWT has been widely used in digital image

steganography due to its multi-resolution characteristics.


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Digital Image Steganography Based on Combination of DCT and DWT 597

In this paper, steganography based on combination of two transforms DWT and

DCT has been described. The proposed technique showed high robustness against

many image processing attacks. The remainder of this paper is organized as follows.

Section 2 presents the related work. Section 3 presents the proposed DCT-DWT based

digital image steganography approach. Section 4 shows the experimentation andresults followed by conclusions in section 5.

2 Related Work

Least Significant Bit Substitution (LSB) [1] is the most commonly used

steganography technique. Sinha and Singh [2] proposed a technique to encrypt an

image for secured transmission using digital signature of the image. Digital signatures

enable the recipient of a message to verify the sender of a message and validate that

the message is intact. In [3], a spatial domain approach, the authors proposed the

exploitation of correlation between adjoining pixels for determining the bit number to

be embedded at certain specific pixel.

In [4], a frequency domain approach, the authors proposed that embedding is

realized in bit planes of subband wavelet coefficients obtained by using the Integer

Wavelet Transform. In [5], authors proposed an algorithm that utilized the probability

density function to generate discriminator features fed into a neural network system

which detected hidden data in this domain. Tsuang-Yuan et al. [6] proposed a new

method for data hiding in Microsoft word documents by a change tracking technique.

Kisik et al. [7] proposed a stegnographic algorithm which embeded a secret messageinto bitmap images and palette-based images. The algorithm divided a bitmap image

into bit plane images from LSB-plane to MSB-plane for each pixel. Satish et al. [8]

proposed a chaos based spread spectrum image steganography method. The majority

of LSB steganography algorithm embed message in spatial domain such as pixel

value differencing [9].

McKeon [10] proposed a methodology for steganography based on fourier domain

of an image by using the properties of zero-padding. These zeros can be changed

slightly where the change in the image is not noticeable. In [11], authors discussed the

effects of steganography in different image formats and DWT. They also introducedthe number of payload bits and the place to embed. In [14], authors proposed method

to spread hidden information within encrypted image data randomly based on the

secret key. Li et al. [17] proposed loseless data hiding using difference value of

adjacent pixels instead of the whole image. Tsai et al. [15] divide the image into

blocks where redual image was calculated using linear prediction. Then, the secret

data was embedded into the residual values, followed by block reconstruction. Chao

et al. [16] presented the embedding scheme that hide secret messages in the vertices

of 3D polygen models.

3 DWT-DCT Based Digital Image Steganography Approach

In this paper, we combine the algorithm [12] with Discrete Cosine Transform (DCT).

The proposed algorithm is as follows.


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598 V. Kumar and D. Kumar

3.1 Embedding Procedure

The steps of embedding procedure are as follows:

1. Apply DCT to the secret image Sto get DCT coefficients.2. Decompose the cover image (Imatrix) and the DCT coefficients of secret image

into four sub-images (ICA, ICH, ICV, ICD) and (CCA, CCH, CCV, CCD)

respectively using DWT.

3. Each ofCCA,ICA, and ICHare partitioned into blocks of4 4 pixels and can be

represented by:

{ }nsiBSCCA i = 1, (1)

ncjBCICA j = 1, (2)

{ }kICH BH ,1 k nc= (3)

where iBS , jBC ,and kBH represent theth

i block in CCA, the thj block in

ICA and theth

k block in ICH respectively. ns is the total number of the4 4 blocks in CCA and nc is the total number of the 4 4 blocks in each of

ICA andICH.

4. For each block iBS in CCA, the best matched block jBC of minimum error in

ICA is searched by using the root mean squared error (RMSE).The first secret

key K1 consists of the addressesj of the best matched blocks inICA.

5. Calculate the error block iEB between iBS and jBC as follows:

iji BSBCEB = (4)

6. For each error block iEB , the best matched block kBH in ICH is searched for

using the RMSE criteria as before and that kBH is replaced with the error

block iEB . The second secret key K2 consists of the addresses k of the best

matched blocks inICH.

7. Repeat the steps 4 to 6 until all the produced error blocks are embedded inICH.

8. Apply the inverse DWT to theICA,ICV,ICD, and the modified sub-imageICH

to obtain the stegano-image G.

3.2 Extraction Procedure

The steps of secret image extraction procedure are as follows:

1. Decompose the stegano-image G into four sub-images (GCA, GCH, GCV, GCD)

using DWT transform.


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2. Extract the block jBC from the sub-image GCA by using the first secret key

K1. Use the second secret key K2 to extract the error blocks. The secret

blocks iBS can be obtained by:

iji EBBCBS = (5)

3. Repeat step 2 until all the secret blocks are extracted and form the sub-image

CCA.4. Using detail coefficients from sender such as CCD, CCV, CCH and extracted

CCA from above step, apply the inverse DWT to obtain the DCT coefficients.

5. Apply the inverse DCT on DCT coefficients obtained from step 4.

4 Experimentation and Results

4.1 Experiment 1 and Results

We evaluate the performance of the combined DCT-DWT based digital image

steganography using four cover images: Peppers, Lena, Goldhill and Boat, each of

size 256 256 and four secret images: Redfort, Watch, C.V. Raman and Taj Mahal,

each of size 128 128. Figure 1 shows all the secret images.

(a) (b) (c) (d)

Fig. 1. Secret images (a) Redfort (b) Watch (c) Raman (d) Taj Mahal

We compare the two techniques, LSB [1] and Ahmed A. Abdelwahab [12] withproposed method using above mentioned four cover images and Redfort as secret

image. The PSNR values of stegano-images after embedding secret image for above

said methods are tabulated in table 1. The results reveal that proposed method has

higher PSNR value than the other two methods.

Table 1. Comparison between LSB [1], Ahmed [12] and Proposed methods in terms of PSNR

using Redfort as secret image

Image PSNR

Cover Image

(256x256)

LSB[1] Ahmed[12] Proposed

method

Peppers 10.75 31.59 42.09

Lena 09.66 31.86 41.93

Goldhill 11.16 31.86 41.84

Boat 12.91 32.37 42.45


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600 V. Kumar and D. Kumar

4.2 Experiment 2 and Results

The next experiment was performed to see the effect of chosen attacks such as

Gaussian Noise, Sharpening, Median Filtering, Gaussian Blur, Histogram

Equalization, Gamma Correction, Transform and Cropping. The PSNR values forfour different stegano images and extracted secret images after different image

processing attacks are illustrated in table 2. The results reveal that after applying

attacks on stegano images, the secret images have high value of PSNR and hence the

good visual quality.

Table 2. PSNR of stegano-images and extracted secret images under different image

processinh attacks

Image PSNR with different attacks

G.

Noise

Sharp. Hist.

Equl.

G. Blur Gamm.

Corr.

Trans-

form

Median

Filter.

Crop.

Stegano-Peppers 19.72 17.15 20.01 26.19 52.80 11.75 27.61 08.69

Extract-Redfort 19.50 17.10 13.26 25.36 12.44 38.92 23.32 09.51

Stegano-Lena 19.89 13.65 16.25 25.42 40.48 11.78 25.37 08.70

Extract-Watch 19.37 31.01 27.08 37.65 36.34 40.03 37.27 30.64

Stegano-Goldhill 18.69 11.95 16.51 25.06 25.54 12.61 24.87 10.07

Extract-Raman 18.66 31.64 30.06 32.68 20.19 38.66 32.38 16.97

Stegano-Boat 19.61 16.10 16.16 19.61 38.42 13.26 26.63 08.83

Extract-Taj 15.52 22.05 20.12 15.52 12.35 38.23 21.19 15.84

5 Conclusion

This paper presented a digital image steganography technique in which DCT was

combined with DWT. The experimentation was done using different attacks. The

simulation results depict that there is substantial increase in the PSNR value of the

stegano images. Further a comparison of proposed technique with the earlier existing

techniques [1] and [12] establishes supremacy of the proposed algorithm.

References

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Digital Image Steganography Based on Combination of DCT and DWT_springer2010