Robust Imperceptible Video Watermarking for MPEG Compression and DA-AD Conversion Using Visual Masking

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1 Robust Imperceptible Video Watermarking for MPEG Compression and DA-AD Conversion Using Visual Masking Sang-Keun Ji, Wook-Hyung Kim, Han-Ul Jang, Seung-Min Mun, and Heung-Kyu Lee School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea, {skji, whkim, hanulj, smmun, Abstract. In this paper, we propose a robust and invisible video watermarking scheme. To ensure robustness against various non-hostile disturbances which can occur during the distribution of digital content, the proposed system selects certain blocks using a robust and imperceptible block selection scheme and watermarks are embedded into these blocks using spread-spectrum watermarking in DCT domain. In addition, visual masking is applied to the watermarking embedding process for high invisibility. Our system is designed to extract 16 bits data in any 15-second interval. Experimental results show that the proposed system offers high invisibility and that it is robust against MPEG-4 compression and DA- AD conversion. Keywords: video watermarking, video compression, DA-AD conversion, visual masking 1 Introduction Digital watermarking is a technique to embed an imperceptible message in the digital cover works such as audio, or video, etc. This watermarking scheme is mainly used for copyright protection [1]. The main properties of digital watermarking are robustness, imperceptibility, and payload. There are various non-hostile disturbances that threaten to degrade the robustness of digital video. These include such as noise addition, compression, and digital/analogue - analogue/digital (DA-AD) conversion [2]. The latter, in particularly, can have such strong effect that it makes other copyright protection techniques, such as digital rights management (DRM), ineffective. Moreover, digital video watermarking should be robust to DA-AD conversion because if it is not, copy-protected videos could easily be duplicated using analogue means [3]. There have been proposals for robust watermarking schemes against DA-AD conversion attack in several papers. Lubin et al. created a watermark pattern at very low frequency and embedded the pattern in both space and time domains Corresponding author

2 satisfying fidelity, robustness, and security [4]. Lee et al. designed a robust watermarking method where the watermark pattern was low-pass filtered because low frequency is less influenced by common signal processing, especially by DA-AD conversion [5]. Imperceptibility is one of the main goals of digital watermarking and it means that the embedded watermark creates insignificant changes to the cover work which cannot be perceived by human visual system (HVS). Generally, high imperceptibility is required to provide high quality watermarked content. Niu et al. proposed visual-saliency-based watermarking to provide high imperceptibility [8]. Visual saliency indicates the degree and location to which human visual attention is most attracted [6]. Kim et al. designed a digital video watermarking scheme using an HVS masking function for high robustness and high invisibility [9]. Kutter et al. presented a watermarking method where the blue channel was the watermark embedding domain [10]. They employed the characteristic that human eyes are least sensitive to the blue channel. In this paper, we propose a video watermarking system with high imperceptibility and robustness against MPEG-4 compression and DA-AD conversion. In order to satisfy these requirements, our system selects significantly imperceptible blocks using a robust and imperceptible block selection method, and a watermark is embedded into these blocks using spread-spectrum watermarking in DCT domain. For robustness against MPEG-4 compression and DA-AD conversion, the watermark should be strongly embedded, though this can decrease the degree of invisibility. Therefore, visual masking is applied to watermarked blocks for high invisibility and a watermark is only embedded in the blue channel of RGB channel because human eyes are less sensitive to changes in the blue channel [10]. In the proposed system, the changes caused by the watermark are not noticeable by human eyes because the robust and imperceptible block selection scheme along with visual masking were applied to ensure good invisibility. The rest of the paper is organized as follows. In Section 2, The proposed watermarking system is described. In Section 3, the experimental setup and results are shown and Section 5 concludes. 2 Proposed Method The overall process of the proposed method consists of watermarking embedding and extraction process as shown in Fig. 3 and Fig. 5. Watermarking embedding process consists of visual masking, the robust and imperceptible block selection, data encoding and watermark embedding. 2.1 Visual Masking Generally, there is a trade-off between the robustness and the imperceptibility of watermarking. In the embedding process, the stronger the watermark, the less imperceptible the watermarked content, and vice versa. However, the strength

3 (a) original image (b) saliency map (c) saliency strength map (d) noise map (e) motion map (f) combined noise map Fig. 1: Examples of Visual Masking of the watermark can be maintained and imperceptibility can be increased if HVS is used. Watermarks are normally not embedded at low frequencies because changes at a low frequency are significantly more visible than those done at a high frequency. Thus, visual masking is utilized to embed high-strength watermarks in less perceptible areas, while low-strength watermarks are embedded in more perceptible areas. We describe the visual masking schemes used to improve the watermarking performance of the proposed method in this section. Visual Saliency Model There are various approaches which can be used to model human visual characteristics, as human visual characteristics are highly complex. Particularly, the visual saliency model (VSM) is a scheme employing the area which are highly attractive to human visual attention. The visual saliency model creates a saliency map using features such as color, intensity, and orientation. In this paper, we create a visual saliency map using Graph-Based Visual Saliency (GBVS), as proposed by Harel et al. [7]. GBVS is a simple and biologically plausible bottom-up visual saliency model which uses a graph-based random walk to reflect human visual characteristics. The process of applying GBVS is as follows. First, the saliency map of a frame is calculated using GBVS. The saliency map tends to be concentrated in a particular range, as shown in Fig. 1(b). For the human visual system in the saliency map, segmentation depending on the distribution of the values on the map is important as opposed to the values themselves. For the segmentation of the saliency map, the cumulative distribution function (CDF) of the saliency map is calculated, and the saliency strength map is then computed considering the CDF, as shown below: α vsm x,y = α 1, F (p x,y ) < 0.5 α 2, 0.5 F (p x,y ) < 0.8 α 3, 0.8 F (p x,y ) (1)

4 where p x,y is the value at (x, y) of the saliency map, and F is the CDF of the saliency map. α 1, α 2, and α 3 are the watermark strength constants, and αx,y vsm is the value at (x, y) of the saliency strength map. The saliency strength map represents the weight map in the watermark embedding process based on the visual saliency model. Noise Visibility Function Voloshynovskiy et al. proposed the HVS function based on the computation of a noise visibility function (NVF) that characterizes the properties of the local image by identifying textured and edge regions [11]. Watermarks are strongly embedded at less attractive regions, while they are lightly embedded at more attractive regions using NVF. However, this method has the disadvantage in that its complexity is very high. To overcome the complexity problem, our system computes a noise map using a simplified NVF with a 2-D linear separable filter with low complexity as proposed by Kim et al. [9]. In addition to this, human eyes are less attracted to moving objects such as driving cars. Thus, a motion map is computed using the differences between video frames based on the time domain, as shown in Fig. 1(e). Then, a binary motion map is calculated considering a predefined threshold. For example, the binary motion value of (x, y) is set to 1 when the motion value of (x, y) is higher than the threshold; otherwise, the value is set to 0. As shown in Fig. 1(f), a noise map and a binary motion map are finally combined to increase the both watermark embedding regions and strength. A combined noise map is used as the weight map in the same manner as a saliency strength map. To use these maps as the weight map, the values of the saliency strength map and the combined noise map are normalized so that they are between 0 and Robust and Imperceptible Block Selection In the proposed method, the watermark is embedded by means of visual masking, as described above. However, visual masking has a limitation in that the watermark extraction accuracy is seriously low when the masking value is too low. For example, the masking value is very low at an area of high attention and a flat area, and this causes the watermark signal to be weak. Therefore, a robust and imperceptible block selection scheme is proposed to overcome this limitation of visual masking. The scheme employs the blocks with the highest masking values to improve the degree of imperceptibility and minimize the data loss caused by compression. The selection process of a robust and imperceptible block is as follows. First, an image is divided into blocks and a NVF map is calculated at every block. Then, m blocks with the highest intensity of a noise map are selected. A saliency strength map is calculated at m blocks and k blocks with the highest strength are selected, where k is the number of necessary blocks to embed watermarks, with k being smaller than m. These selected blocks are more imperceptible than other blocks because visual masking is applied. Moreover, these blocks are more robust against the data loss as compared to other blocks because the watermark strength of the selected blocks is higher than that of the other blocks.

5 An example of the correlation of the blocks in uncompressed and compressed videos is in Fig. 2. In this example, the frame was divided into 16 blocks, and the watermarked video explained in section 3.1 is is compressed by MPEG-4 part10 (H.264) with 1,200Kbps. Red dots represent 5 blocks with the highest strength using a robust and imperceptible block selection method, and blue dots represent 5 blocks with the lowest strength. As shown in Fig. 2, blocks with the highest strength tend to have a higher correlation compared to other blocks. Fig. 2: An example of the correlation of the blocks The watermark accuracy and robustness increase when k increases; however, the imperceptibility decreases. In contrast, the watermark accuracy and robustness decrease when k is low. Therefore, we experimentally determined the number of necessary blocks and obtained high robustness and high imperceptibility. 2.3 Data Encoding In the proposed system, watermark patterns are designed to represent 16 bits data. To this end, two sets, X and Y, of pseudo-random sequences are used as the watermark. Each set is composed of 256(= 2 8 ) pseudo-random sequences to represent 8 bits data, as expressed by the following equation. X = {X 0, X 1, X 2,..., X 255 }, Y = { Y 0, Y 1, Y 2,..., Y 255 }. (2) To represent 16 bits data, the sequences X i and Y j are selected from X and Y. Here, X i represents the upper 8 bits data and Y j represents the lower 8 bits data. By attaching them, two pseudo-random sequences can present 16 bits

6 data. For example, if the data is 0x00F F, then X i corresponds to a sequence representing 0x00 from X and Y j corresponds to a sequence representing 0xF F from Y. Each pseudo-random sequence of X i and Y j is an N-length pseudorandom sequence, as determined by the equation below. X i = {x i1, x i2,..., x in }, Y j = {y j1, y j2,..., y jn }. (3) 2.4 Watermark Embedding Robust and Imperceptible Block Selection Data Encoding Visual Saliency Model Noise Visibility Function Saliency Map Noise Map Block Selection 16-bit data Sequence Generator Pseudo-random sequence sets Two watermarks X, Y Watermark Embedding Original Video Selected Blocks Watermarked Video Watermarked Unselected Blocks Saliency-based Watermarked Blocks x + x Inverse Saliency Map Saliency Map DCT Watermark Embedding Inverse Noise Map Noise Map x + x Noise-based Watermarked Blocks Inverse DCT Averaging Watermarked Blocks Fig. 3: Watermark embedding process In the proposed watermarking embedding scheme, there are two main steps. First, blocks are selected using the robust and imperceptible block selection method. Then, the watermarking embedding process is then used for each selected block. The overall process of watermarking embedding is shown in Fig. 3.

7 First, the blocks which are used for watermark embedding are selected by means of robust and imperceptible block selection for invisibility. Then, two pseudo-random sequences X i and Y j are embedded into each half of the selected blocks. In the proposed method, the selected blocks are sorted according to visual masking values, and the sequence X i is embedded into odd-ordered blocks, with Y j embedded into even-ordered block. For each of the selected blocks, the watermark embedding precess is performed as follows. First, spread-spectrum watermarking in the DCT domain [12] is used for robustness. In each block, the discrete cosine transform is calculated and the vector V having coefficients of length N is extracted from a fixed point. A pseudo-random sequence is then embedded into the vector V via the following equation: v i = v i + α v i w i, (0 i N) (4) where V i = {v 1, v 2,..., v n } is the vector of the original coefficients and V i = {v 1, v 2,..., v n} is the vector of the watermarked coefficients. α is the strength of the watermark signal. W = {w 1, w 2,..., w n } is a pseudo-random sequence from X or Y in the selected block. In the DCT domain, coefficients of length N are selected from the mid-frequency coefficients for robustness of compression and for good visual quality. After embedding a sequence, inverse DCT is performed and the watermarked block B is obtained. An example of a watermarked frame is in Fig. 4. In Fig. 4, the number of blocks is 16 and the number of selected blocks k is 6, a sequence X i is embedded into blocks with red line, and a sequence Y j is embedded into blocks with blue line. White regions of left-top corner indicates watermark embedding regions for each block. Fig. 4: An example of watermark embedding in selected blocks Finally, visual masking is applied to the watermarked blocks to enhance the invisibility. The visual masking method is

8 B s = I s B + (1 I s ) B, B n = I n B + (1 I n ) B, B w = (B s + B n )/2 (5) where B denotes the original block of the selected block and B is the watermarked block of the selected block. In addition I s and I n represent the value of the saliency map and the combined noise map, respectively. The bigger these values are, the stronger the watermarks are embedded. Finally, the masked watermarked block B w is the average of B s and B n. Masking using a combined noise map can achieve high transparency, but it incurs a large amount of information loss, which can decrease the robustness. In contrast, masking using a saliency strength map inserts the watermark more strongly compared to the use of a combined noise map. Therefore, masking is better when these two characteristics are balanced by averaging. This embedding process is repeated in each selected block. A frame is then reconstructed by combining the selected blocks B w and the unselected blocks, and this process is repeated for each frame. The proposed method selects the blocks in which to embed watermarks, and it provides higher imperceptibility than a method which uses all of the blocks. The watermarks are embedded strongly to prevent any decreases in the robustness; however, the traces are imperceptible due to visual masking. Also, watermark losses are minimized because blocks with low masking values are excluded. Therefore, the proposed method achieves high robustness and high imperceptibility. 2.5 Watermark Extraction The watermark extraction process is shown in Fig. 5. First, a frame is divided into blocks and the DCT coefficients of each block are calculated. Then, an N- length vector V is extracted from the DCT coefficients at a fixed point. During the watermark extraction process, a correlation is used to determine whether a watermark is embedded or not. The correlation is computed between vector V and all sequences from the two sequence sets X and Y via the following equation: z = W V N = 1 N N w i vi. (6) where z is the correlation value and W is a pseudo-random sequence from X and Y. By comparing the correlation z and the threshold, the presence or absence of the watermark W is determined. The threshold is calculated as T = 1 15 N i=1 N vi. (7) i=1

9 The threshold is determined by experimental results to minimize a falsepositive rate. After correlation computation of each block, the existence of a watermark is determined by the following watermark extraction rule. The result of watermark extraction of a frame is calculated using the extraction results of all blocks in a frame. The watermark extraction rule of a frame is as follows. Rule 1. Watermark extraction rule of a frame if (The total number of detected blocks!= 2) then Watermarks are not detected else (The total number of detected blocks == 2) if two watermarks are in the same set of watermark then Watermarks are not detected else (one is in the set of X and the other is in the set of Y ) then Watermarks are detected end end For example, if sequences that are over the threshold are X 10, X 20, Y 128, it means that a watermark is not detected. Also, if sequences that are over the threshold are Y 48, Y 241, it describes that a watermark is not detected. In contrast, if sequences that are over the threshold are X 149, Y 214, it means that a watermark is detected. In this way, the watermark extraction rule of a frame is applied to each frame. Watermarked Video Blocks DCT Pseudo-random sequence sets Watermark Extraction 16-bit data Data Decoding Two watermarks X, Y Fig. 5: Watermark extraction process

10 3 Experimental Results 3.1 Experimental Environment Experimental Setup We simulated our method using the sample video set IWIHC provided, as shown in Fig.7. (display resolution: 1920x1080, 30 frames per seconds, 30 seconds, 12Gbps, 8bits depth uncompressed AVI files) The proposed watermarking algorithm was implemented in the MATLAB R2013a environment, and a computer of Intel i7-4770k (3.50 GHz) with 16 GB main memory was used to evaluate the performance. We used Fosmon HDMI to component video (YPbPr) / VGA & SPDIF output converter box that supports 1280x720 resolution as a digital-to-analogue (DA) converter and skyhd capturex HDMI that supports real-time full high definition (FHD) recording as an analogue-to-digital (AD) converter. The important point is that the video after DA-AD conversion has different resolution from that of the original video since the maximum resolution of a DA converter is 1280x720. The number of a robust-and-imperceptible block k and the number of the selected block in a noise map m were experimentally set to 6 and 8 respectively. α 1, α 2 and α 3 used in section 2.1 were set to 0.7, 0.6 and 0.5. Also, 25, 000 coefficients represented by N in section 2.3 were selected at the fixed location in the DCT domain. The determined coefficients were robust to scaling caused by DA-AD conversion because the coefficients were used at middle frequency. The experiments were implemented to test the visual quality and robustness. We used peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [13] to evaluate imperceptibility, and we used bit error rate (BER) to evaluate the watermarking performance. The proposed watermarking scheme is designed to have robustness to MPEG- 4 compression and DA-AD conversion. The overall process of experiments is as shown in Fig. 6. First, we embedded watermarks into videos using the proposed watermark embedder and compressed the watermarked video using MPEG-4 part10 (H.264) codec that FFmpeg provided. Then, the compressed video was transformed to analogue signal by a DA converter. The input of a DA converter was a digital signal through high definition multimedia interface (HDMI) and the output was an analogue component video formed by analogue color space YPbPr through a component cable. After that, we converted an analogue YPbPr signal to a digital signal using an AD converter and saved the digital signal as uncompressed YUV422 format. Robustness to MPEG-4 compression and DA- AD conversion was evaluated calculating BER of the digital signal. We embedded the same watermark with 16 bits to all frames in 15 seconds. 3.2 Image Quality We performed experiments to determine the relation between the robustness and an image quality in the proposed watermark embedding algorithm by changing various watermark strengths α.

11 Test Video MPEG-4 encoder Compressed Original Video MPEG-4 decoder PSNR Watermark Embedder Watermarked Video MPEG-4 encoder Compressed Watermarked Video MPEG-4 decoder SSIM D-A converter 16-bit data BER A-D converter Watermark Exractor Fig. 6: Test process in the proposed watermarking system Fig. 7: Compressed original images (first row), and compressed watermarked videos for α = 0.4 (second row) (bit rate = 12,000Kbps, compressibility = 1/100) To evaluate image quality, the following process was performed, as shown in Fig. 6. The watermarked video and the original video were compressed with the same bit rate by using the MPEG-4 part 10 (H.264) codec. In this paper, the size of the compressed bit stream were set to have 1/100 of the original bit stream. After that, two compressed video were decompressed and the luminance channel Y was calculated from the RGB channel according to the following equation. Y = G B R (8) Then, the PSNR and SSIM were calculated for each pair of luminance channels. Because the original video has a bit rate of 12Gbps, the watermarked video was compressed to have a bit rate of 12Mbps, which is 1/100 of the bit rate of the original video. Also, the BER(Bit Error Rate) was calculated to represent the relation between image quality and the robustness to compression. For exact data

12 extraction without errors, BER should be zero during the watermark extraction process. We consider the results having a non-zero BER as a failure in watermark extraction. WM strength α PSNR(dB) Basketball SSIM BER PSNR(dB) Lego SSIM BER PSNR(dB) Library SSIM BER PSNR(dB) Walk1 SSIM BER PSNR(dB) Walk2 SSIM BER PSNR(dB) average SSIM BER : watermark is not detected Table 1: Test Result at various watermark strength α for MPEG-4 with 12,000Kbps Table 1. shows the PSNR, SSIM, and BER results at various watermark strengths α for MPEG4 compression with 12,000Kbps. As results, there is no bit error if α is larger than or equal to 0.3. When the watermark is robust to MPEG-4 compression with a 12,000Kbps bit rate, the minimum of an average PSNR is 43.92dB and the minimum of an average SSIM is In the case of the Walk2 video, there is a great deal of data loss in MPEG-4 compression and the PSNR and SSIM are lower than for other videos because the textured and edge regions are larger than those of other videos. Also, the experiment shows that there is little change in the PSNR and SSIM with increasing α because of using a robust and imperceptible block selection method. 3.3 Robustness To protect the copyrights of the digital HDTV contents, a watermarking scheme should be robust to various attacks such as compression and DA-AD conversion. Also, during DA-AD conversion, the scaling of frames inevitably occurs. In the

13 proposed watermarking embedding algorithm, we select middle-low frequencies to embed the watermark and the watermarked is embedded the fixed points of DCT coefficients. Therefore, the proposed method is robust to attacks that eliminate high frequencies, such as compression and scaling. In this paper, the watermarking system is designed to extract 16 bits data within any 15-seconds period of video. In order to achieve this requirement, two watermarks should be extracted within any 7.5 seconds in the proposed method. If two watermarks are not extracted within 7.5 seconds, we consider that the watermark is not detected. An experiment was performed to evaluate the robustness to compression and DA-AD conversion at various watermark strength α while decreasing the bit rate in compression. We selected α = 0.3, 0.7, 1.5 to determine the relation between the bit rate and α. Table 2, 3 and 4 show the BER in various bit rates with fixed α. When α = 0.3, the embedded data is not extracted except for a 1,200Kbps bit rate. When α = 1.5, the embedded data is exactly extracted within 1/1000 compressibility. Also, we determined that there is little change in the PSNR and SSIMS as α is increased. Bit rate 12,000 8,000 6,000 3,000 1,500 1,200 Compressibility 1/100 1/150 1/200 1/400 1/800 1/1000 Basketball BER Lego BER Library BER Walk1 BER Walk2 BER Average BER : watermark is not detected Table 2: Robustness test results for α = 0.3 ( PSNR db, SSIM ) Bit rate 12,000 8,000 6,000 3,000 1,500 1,200 Compressibility 1/100 1/150 1/200 1/400 1/800 1/1000 Basketball BER Lego BER Library BER Walk1 BER Walk2 BER Average BER : watermark is not detected Table 3: Robustness test results for α = 0.7 ( PSNR db, SSIM )

14 Bit rate 12,000 8,000 6,000 3,000 1,500 1,200 Compressibility 1/100 1/150 1/200 1/400 1/800 1/1000 Basketball BER Lego BER Library BER Walk1 BER Walk2 BER Average BER : watermark is not detected Table 4: Robustness test results for α = 1.5 ( PSNR db, SSIM ) 4 Conclusion This paper has proposed a watermarking system to be robust to MPEG-4 compression and DA-AD conversion with high transparency. The watermarking system employs a robust-and-imperceptible block selection method we propose and a spread spectrum watermarking method in DCT domain. Also, we apply a visual masking method that fuses a saliency map based on visual saliency model and a noise map based on a noise visibility function. As a result, the proposed scheme has high imperceptibility. The experimental results show robustness to compression and DA-AD conversion as well as high imperceptibility with high PSNR and SSIM. Also, the results indicate that the proposed watermarking system extracts 16 bits data in every 15 seconds. However, the proposed method has a disadvantage that it is weak to geometrical attacks because it uses spread spectrum watermarking based on DCT domain. Therefore, the improvement to be robust to geometrical attacks should be needed. Acknowledgements This research project was supported by Ministry of Culture, Sports and Tourism (MCST) and from Korea Copyright Commission in References 1. Cox, I. J.: Digital watermarking and steganography. Elsevier Science Limited (2008) 2. Doërr, Gwenaël and Dugelay, Jean-Luc: A guide tour of video watermarking. Signal Processing: Image Communication, vol. 18, no. 4, pp (2003) 3. Diehl, E. and Furon, T.: Watermark: Closing the analog hole IEEE International Conference on Consumer Electronics, pp (2003)

15 4. Lubin, Jeffrey and Bloom, Jeffrey A. and Cheng, Hui: Robust content-dependent high-fidelity watermark for tracking in digital cinema. SPIE 2003, pp (2003) 5. Lee, Min Jeong and Kim, Kyung Su and Lee, Hae Yeoun and Oh, Tae Woo and Suh, Young Ho and Lee, Heung Kyu: Robust watermark detection against D-A/A- D conversion for digital cinema using local auto-correlation function. Proceedings of International Conference on Image Processing, pp (2008) 6. Itti, L. and Koch, C. and Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp (1998) 7. Harel, Jonathan and Koch, Christof and Perona, Pietro: Graph-based visual saliency. Advances in neural information processing systems, pp (2006) 8. Niu, Yaqing and Kyan, Matthew and Ma, Lin and Beghdadi, Azeddine and Krishnan, Sridhar: A visual saliency modulated just noticeable distortion profile for image watermarking. European Signal Processing Conference, pp (2011) 9. Kim, Kyung Su and Lee, Hae Yeoun and Im, Dong Hyuck and Lee, Heung Kyu: Practical, real-time, and robust watermarking on the spatial domain for highdefinition video contents. IEICE Transactions on Information and Systems, vol. E91-D, no. 5, pp (2008) 10. Kutter, M and Jordan, F and Bossen, F: Digital watermarking of color images using amplitude modulation. Journal of Electronic Imaging, vol. 3022, pp (1998) 11. Voloshynovskiy, S., Herrigel, A., Baumgaertner, N., and Pun, T.: A stochastic approach to content adaptive digital image watermarking. Information Hiding. Springer Berlin Heidelberg, pp (2000) 12. Mauro Barni, Franco Bartolini, Vito Cappellini, and Alessandro Piva.: A DCTdomain system for robust image watermarking. Signal Process, vol. 66, pp (1998) 13. Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P.: Image quality assessment: from error visibility to structural similarity. Image Processing, IEEE Transactions on, 13(4), pp (2004)