An Optimized Un-compressed Video Watermarking Scheme based on SVD and DWT

Bhavna Goel Department of Computer Science,

Ajay Kumar Garg Engineering College, Ghaziabad, India

bhavnagl3@gmail.com

Charu Agarwal Department of Computer Science,

Ajay Kumar Garg Engineering College, Ghaziabad, India

charuagarwal19@yahoo.com

Abstract— In this paper, we presents a novel fast and robust video watermarking scheme for RGB uncompressed AVI video sequence in discrete wavelet transform (DWT) domain using singular value decomposition (SVD). For embedding scene change detection is performed. The singular values of a binary watermark are embedded within the singular values of the LL3 sub-band coefficients of the video frames. The resultant signed video exhibits good quality. To test the robustness of the proposed algorithm six different video processing operations are performed. The high computed PSNR values indicate that the visual quality of the signed and attacked video is good. The low bit error rate and high normalized cross correlation values indicate a high correlation between the extracted and embedded watermark. Time complexity analysis shows that the proposed scheme is suitable for real time application. It is concluded that the embedding and extraction of the proposed algorithm are well optimized. The algorithm is robust and shows an improvement over other similar reported methods.

Keywords— Digital video watermarking, DWT, SVD, BER, Normalized cross-correlation

I. INTRODUCTION

With the rapid development of digital and network technology, humans can easily access, copy, edit and distribute the digital multimedia. Therefore, it is important to protect the intellectual property rights of the owner of digital work [1]. Digital Watermarking is the technique for embedding some cryptographic information into the multimedia elements such as text, images, video and audio to protect the copyright information. It provides the authenticity of intellectual property rights.

Digital video watermarking can be done in compressed domain as well as uncompressed domain. An effective video watermarking scheme ensures three fundamental requirements: robustness, transparency and security. Robustness means the watermark should be resist from various attacks including frame dropping, frame averaging, cropping, rotation, scaling, compression etc. by an attacker. Transparency represents the invisibility of embedded watermark in video stream without degrading the perceptual fidelity of video. Security represents the protection of watermark to resist attempts by an attacker who try to remove it via cryptanalysis

without affecting or modifying the video [2]. A variety of digital video watermarking scheme have been proposed in the literature.

Zhang et. al. [3] proposed the robust video watermarking scheme of H.264/AVC video coding standard. In their scheme, they first modify the grayscale watermark to accommodate the H.264/AVC computational constraints, and then embed it into video data in the compressed domain. They claim that their video watermarking scheme can achieve high robustness and good visual quality without increasing the overall bit-rate.

Mansouri et. al. [4] proposed the blind H.264 compressed domain watermarking scheme. They used the syntactic elements of compressed bit stream for performing the embedding/extraction process. They present a spatiotemporal analysis that selects the appropriate submacroblocks for embedding. They claim that their method prevents bit-rate increase and restricts it within an acceptable limit by selecting appropriate quantized residuals for watermark insertion.

Preda et. al. [5] proposed the novel digital watermarking method for video based on a multi-resolution wavelet decomposition. In their scheme, they used the binary image watermark i.e. embedded into the wavelet coefficients of the LH, HL and HH sub-bands of the second wavelet decomposition level by quantization.

Wang et. al. [6] have developed a real-time video watermarking scheme for MPEG-1 and MPEG-2 video. In their proposed scheme they embed the watermark into the histogram bins calculated from the low-frequency sub band of the DWT domain. They claim that their scheme provide high robustness and transparency with geometric distortions including rotation with cropping, scaling, frame dropping, aspect ratio change and swapping. They also claim that their scheme can also be applied for MPEG-4 and H.264 format.

Masoumi et. al. [7] proposed the blind digital video watermarking method based on multi-resolution wavelet decomposition that uses the scene change analysis to embed the watermark into the LH, HL and HH wavelet coefficients of detected motion scene frames by using pseudo-random numbers. Their experimental result shows the robustness and the invisibility of the embedded watermark against lots of attacks, containing frame averaging, frame swapping, frame dropping and lossy compression.

978-1-4799-0192-0/13/$31.00 ©2013 IEEE 307

Leelavathy et. al. [8] proposed scene based blind video watermarking based on discrete multiwavelet Transform (DMWT) and Quantization Index Modulation (QIM). Using QIM, the logo watermark is embedded into the selected multiwavelet coefficients by quantizing the coefficients and scrambled watermarks are generated using a set of secret keys, and each watermark is embedded in each scene of the video.

In the present work, we propose a video watermarking scheme based on DWT-SVD. The singular values of the LL3 sub-band coefficients are modified by the singular values of the binary watermark image. The paper is organized as follows: Section II describes the proposed scheme including the scene change detection, watermark embedding algorithm and extraction algorithm. Section III details the experimental result and discussion that includes the robustness estimation of proposed scheme and comparative analysis with existing techniques. Finally, the proposed work is concluded in Section IV.

II. PROPOSED METHOD

In the present section, we describe our proposed DWT-SVD video watermarking technique in uncompressed AVI format. A binary image is used as watermark. In our technique, the watermark is embedded directly into the blue component of RGB frame because blue component is less sensitive to human visual system.

A. Scene Change Detection In the proposed scheme histogram difference method is used for scene change detection which is given by Listing 1.

Listing 1: Scene Change Detection Algorithm

Step1. Calculate the histogram of the red component of all the frames. Step2. Calculate the total difference of the whole histogram using the formula given by Eqn. 1

(1)

where is the histogram value for the red component y in the xth frame. Step3. If a scene change is detected.Step4. Maximum of divided by 3 is considered as threshold value so that adaptive frames for embedding the watermark can be achieved.

B. Watermark Embedding Watermark is embedded in the wavelet coefficients of low

frequency sub-band. Fig.1 shows the block diagram of proposed video watermark embedding process. In the present work we consider the host video of size M×N and the watermark W of size n × n. The watermark embedding process is given by Listing 2.

Figure 1. Block diagram of proposed watermark embedding scheme

Listing 2: Watermark Embedding Algorithm

Step1. Apply scene change detection algorithm to detect the scenes (m) from the original RGB video frames. Step2. Decompose the watermark W into m watermark images such as W1, W2, W3….Wm, where the corresponding watermark image is used to modify the frames of corresponding scene. Step3. Apply SVD on each watermark image to obtain the singular values of jth watermark image.

(2)

where j=1,2,3……m Step4. For each scene (j = 1, 2 …, m) perform the following:

1) Apply 3 level DWT using HAAR filter on the blue component of every frame of the jth scene to obtain the LL3 sub-band coefficients.

2) Apply SVD on sub-band coefficients to obtain the singular values .

(3)

where i denotes the sequence of the frames in jth

scene. 3) Embed into using the formula given by

Eqn. 4 (4)

where K is the scaling factor or watermarking strength. In the present work, we take K = 50.

4) Compute the watermarked sub-band coefficients using the formula given by Eqn. 5

(5)

Original video

frames

Scene change

detection

3-level DWT SVD

Watermark Embedding algorithm

3-level IDWT

ISVD

Watermarked video frames

Watermark W

SVD

partition into

m

LL3

Watermarked video

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5) Apply inverse 3-level DWT to the modified sub-band coefficients to obtain the watermarked blue component of the frame.

6) Replace the original blue component in RGB frame by the watermarked blue component to obtain the watermarked video.

Figure 2. Block diagram of proposed watermark extraction scheme

C. Watermark Extraction Fig.2 shows the block diagram of watermark extraction

scheme. The watermark extraction process is given by Listing 3.Listing 3: Extraction Algorithm

Step1. Apply scene change detection algorithm to detect the scenes (m) from the obtained watermarked RGB video frames. Step2. For each scene (j = 1, 2 …, m) perform the following:

1) Apply 3 level DWT using HAAR filter on the blue component of every frame of the jth scene of the watermarked video and original video to obtain the LL3’ and LL3 sub-band coefficients respectively.

2) Apply SVD on and sub-band coefficients to obtain the singular values and respectively.

(6) (7)

where i denotes the sequence of the frames in jth

scene. 3) Extract the watermarked singular values using the

formula given by Eqn. 8

(8)

4) Compute the extracted watermark image for jth scene using the formula given by Eqn. 9

(9)

Step3. Construct the extracted watermark from the computed extracted watermark image to obtain the single watermark image .

The imperceptibility of watermarked frame is measured by computing a full-reference metric known as the peak signal-to-noise ratio (PSNR) which is defined by the formula given by Eqn. 10

where is the watermarked frame and is original frame.

(10)

The average PSNR of all frames of the video is given by Eqn. 11

(11)

where nf is the total number of frames in the video sequence.

After watermark extraction normalized correlation and bit error rate (BER) is computed between extracted watermark and original watermark by using the formula given by Eqn.12 and Eqn. 13 respectively.

(12)

(13)

where W* is the extracted watermark and W is the original watermark.

The signed video frames are also examined for robustness by executing six different spatial attacks. For this purpose, the watermarks embedded in the frames are extracted and matched with original watermarks. BER and NC (W, W*) parameters are computed between original and extracted watermarks. The attacks used in the present work are: (1) Scaling with cropping (by 10%), (2) Adding Gaussian noise (with mean = 0 and variance 0.001), (3) Median filtering (by neighborhood 3×3), (4) Gaussian low-pass filter (by neighborhood 5×5 at standard deviation 0.5), (5) Circular average filter (by radius 5) and (6) Contrast enhancement. These results are compiled and discussed in Section III.

III. EXPERIMENTAL RESULTS AND DISCUSSION

The performance of the proposed scheme is evaluated on two standard video sequences namely: News and Foreman in RGB uncompressed AVI format, of size 352 × 288 and frame rate of 30 fps. Each video sequences consists of 300 frames. A binary image of size 64×64 is used as watermark.

Fig. 3(a-b) depicts the original frame of the two video sequences and Fig. 3(c) depicts the binary watermark. Fig. 4(a-b) depicts the signed frames corresponding to the frames

Watermarked video frames

Scene change

3-level DWT

SVD

Watermark Extraction algorithm

Collect the watermark

images

ISVD

Watermark W*

S*i(j) Sw*(j)

W*(j)

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shown in Fig. 3(a-b) respectively. Fig. 5(a-b) shows the binary watermark extracted from the two video sequences of Fig. 4(a-b) respectively. The computed values of BER and NC (W, W*) parameters of the extracted watermarks are placed on top of them.

(a) (b)

(c)

Figure 3. (a) 1st original frame of News, (b) 1st original frame of Foreman, (c) Original watermark.

(a) (b) Figure 4. (a) 1st watermarked frame of News (45.66 dB), (b) 1st watermarked frame of Foreman (42.37 dB).

BER =0.00 BER =0.00 NC (W, W*) =1 NC (W, W*) =1

(a) (b)

Figure 5. (a) Extracted watermark from News, (b) Extracted watermark from Foreman.

It is clear from the Fig.4 that with the proposed algorithm, we have obtained high PSNR values for both the videos. Similarly, we also obtained BER = 0 and NC (W, W*) = 1 as shown in Fig.5 which indicate a high degree of correlation between the embedded and extracted watermark. To examine the robustness of the proposed algorithm six different attacks are performed on signed video frames. These are: (1) Scaling with cropping (by 10%), (2) Adding Gaussian noise (with mean = 0 and variance 0.001), (3) Median filtering (by neighborhood 3×3), (4) Gaussian low-pass filter (by

neighborhood 5×5 at standard deviation 0.5), (5) Circular average filter (by radius 5) and (6) Contrast enhancement. Fig. 6(a-l) depicts the attacked signed frame of News and Foreman video corresponding to the frames shown in Fig 4(a-b). Fig. 7(a-l) depicts the extracted watermark from the two attacked signed video sequences News and Foreman. The PSNR, NC (W, W*) and BER (%) values obtained by applying the above said attacks on the two watermarked videos sequences used in this work are tabulated in Table I. The results compiled in Table I clearly suggest that the computed values of PSNR, NC (W, W*) and BER (%) obtained in case of attacks are within the expected range.

(a) (b)

(c) (d)

(e) (f)

(g) (h)

310

(i) (j)

(k) (l)

Figure 6. 1st frame of News and Foreman under different attacks (a-b) 10% scaling, (c-d) Gaussian noise with mean 0 and variance 0.001, (e-f) 3 × 3 median filter, (g-h) 5 × 5 gaussian filter, (i-j) Circular filter with radius 5, (k-l) Contrast enhancement.

(a) (b)

(c) (d)

(e) (f)

(g) (h)

(i) (j)

(k) (l)

Figure 7. Extracted watermark from News and Foreman under different attacks (a-b) 10% scaling, (c-d) Gaussian noise with mean 0 and variance

0.001, (e-f) 3 × 3 median filter, (g-h) 5 × 5 gaussian filter, (i-j) Circular filter with radius 5, (k-l) Contrast enhancement.

TABLE I. PSNR, NC (W, W*) and BER (%) values for attacked Foreman and News video sequences

Attacks Foreman News

PSNR(dB)

NC .BER(%)

PSNR (dB)

NC BER(%)

Scaling with cropping 10% 15.59 0.84 3.61 13.07 0.72 7.98 Gaussian noise var = 0.001 29.76 1 0.00 29.90 1 0.00 Median filter 3×3 33.34 1 0.00 31.71 1 0.02 Gaussian filter 5×5, =0.5 36.79 1 0.00 37.49 1 0.02 Circular filter radius = 5 21.40 0.84 8.52 23.49 0.98 3.32 Contrast Enhancement 24.46 1 2.51 23.07 0.99 2.61

To estimate the time complexity of proposed algorithm, we compile the embedding and extraction time taken by News and Foreman video sequences for 300 frames in Table II. Note that these computed time spans are of the order of few seconds only. Thus, the present work proposes a watermarking scheme which is a successful candidate for implementing digital watermarking in video sequences on a real-time scale.

TABLE II. Time (in seconds) taken by proposed algorithm

Foreman News

Embedding time (sec) 17.7217 16.5205

Extraction time (sec) 8.3617 7.7532

To further study the performance of the proposed video watermarking algorithm, we compared the results of the proposed algorithm with the reported results of the Zhang et. al. [3]. Table-III compiles the PSNR and NC values for different attacks to compare our method with Zhang’s method.

TABLE III. Comparison of PSNR and NC(W,W*) values of proposed method and Zhang et. al.[3] method.

Attacks Zhang’s method

[3] Proposed method

NC PSNR(dB) NC PSNR(dB)No Attack 0.93 41.00 1 42.37 Gaussian noise (var=0.001) 0.75 31.04 1 29.76 Gaussian filter [5 5] 0.72 35.73 1 36.79 Circular filtering (r=5) 0.87 23.04 0.98 23.49 Contrast enhancement 0.83 22.91 1 24.46

It is clear from the Table III that our method outperforms Zhang’s method in all the five cases. The reported value of our

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PSNR is itself indicative of good visual quality of signed video sequences. Also, the reported NC (W, W*) values after executing attacks are better in our case than those reported by Zhang et. al. [3]. We, therefore, conclude that the proposed watermark embedding and extraction algorithm is well optimized. As the time complexity of this algorithm is very small, our watermarking algorithm offers good practical applications especially on real time scale.

IV. CONCLUSION

In the present work, a novel fast and robust DWT-SVD based video watermarking algorithm is proposed. The singular values of the LL3 sub-band coefficients are modified by the singular values of the binary watermark image. The low time complexity of proposed algorithm makes it suitable for watermarking of video on a real time scale. The computed values of all parameters are within the expected range. The perceptible quality of the video frames is very good as indicated by high PSNR values. Watermark recovery is also found to be good as indicated by high cross correlation values and low bit error rate between embedded and extracted watermarks. It is concluded that the embedding and extraction of the proposed algorithm are well optimized. The algorithm is robust and shows an improvement over other similar reported methods.

REFERENCES

[1] Hui-Yu Huang, Cheng-Han Yang, and Wen-Hsing Hsu,” A Video Watermarking Technique Based on Pseudo-3-D DCT and Quantization

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[2] Biswas, S., Das, S.R., and Petriu, E.M.,” An adaptive compressed MPEG-2 video watermarking scheme,” IEEE Transactions on Instrumentation and Measurement Vol. 54, No. 5, 2005.

[3] Jing Zhang, Anthony T. S. Ho, Gang Qiu and Pina Marziliano, “Robust Video Watermarking of H.264/AVC,” IEEE Transactions on Circuits and Systems—Ii: Express Briefs, vol. 54, no. 2, February 2007.

[4] Azadeh Mansouri, Ahmad Mahmoudi Aznaveh, Farah Torkamani-Azar, and Fatih Kurugollu, “A Low Complexity Video Watermarking in H.264 Compressed Domain,” IEEE Transactions on Information Forensics and Security, vol. 5, no. 4, December 2010.

[5] Radu O. Preda, Dragos N. Vizireanu,” A robust digital watermarking scheme for video copyright protectionin the wavelet domain,” Measurement 43 (Elsevier) Volume 43, Issue 10, December 2010, Pages 1720–1726.

[6] Liyun Wang, Hefei Ling, Fuhao Zou, and Zhengding Lu, “Real-Time Compressed-Domain Video Watermarking Resistance to Geometric Distortions,” IEEE Multimedia 2012.

[7] Majid Masoumi and Shervin Amiri, “A Blind Video Watermarking Scheme Based on 3D Discrete Wavelet Transform,” International Journal of Innovation, Management and Technology, vol. 3, no. 4, August 2012.

[8] N. Leelavathy, E. V. Prasad and S. Srinivas Kumar,” A Scene Based Video Watermarking in Discrete Multiwavelet Domain,” International Journal of Multidisciplinary Sciences and Engineering, vol. 3, no. 7, July 2012.

[9] Raghavendra K, Chetan K. R., “A Blind and Robust Watermarking Scheme with Scrambled Watermark for Video Authentication,” in Proc. IEEE Int. Conf. on Internet Multimedia Services Architecture and Applications (IMSAA), 2009.

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