New Video Watermark Scheme Resistant to Super Strong Cropping Attacks

Ming Tong, Tao Chen, Wei Zhang, Linna Dong

School of Electronic Engineerin, Xidian University, Xi’an, China.

DOI: 10.4236/jis.2012.32016                

Abstract

Firstly, the nonnegative matrix factorization with sparseness constraints on parts of the basis matrix (NMFSCPBM) method is proposed in this paper. Secondly, the encrypted watermark is embedded into the big coefficients of the basis matrix that the host video is decomposed into by NMFSCPBM. At the same time, the watermark embedding strength is adaptively adjusted by the video motion characteristic coefficients extracted by NMFSCPBM method. On watermark detection, as long as the residual video contains the numbers of the least remaining sub-blocks, the complete basis matrix can be completely recovered through the decomposition of the nonnegative matrix of the least remaining sub-blocks in residual videos by NMFSCPBM, and then the complete watermark can be extracted. The experimental results show that the average intensity resistant to the various regular cropping of this scheme is up to 95.97% and that the average intensity resistant to the various irregular cropping of this scheme is up to 95.55%. The bit correct rate (BCR) values of the extracted watermark are always 100% under all of the above situations. It is proved that the watermark extraction is not limited by the cropping position and type in this scheme. Compared with other similar methods, the performance of resisting strong cropping is improved greatly.

Keywords

Digital Watermarking; Cropping Attack; Geometric Attacks; Nonnegative Matrix Factorization (NMF); Sparseness Constrain

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	H. Zhang, et al., “Affine Legendre Moment Invariants for Image Watermarking Robust to Geometric Distortions,” IEEE Transactions on Image Processing, Vol. 20, No. 8, 2010, pp. 2189-2199. doi:10.1109/TIP.2011.2118216
[2]	C. V. Serdean, M. A. Ambroze, M. Tomlinso and J. G. Wade, “DWT-Based High-Capacity Blind Video Watermarking, Invariant to Geometrical Attacks,” IEEE Proceedings of the Vision, Image and Signal Processing, Vol. 150, No. 1, 2003, pp. 51-58. doi:10.1049/ip-vis:20030 159
[3]	L. E. Coria, M. R. Pickering, P. Nasiopoulos and R. K. Ward, “A Video Watermarking Scheme Based on the Dual-Tree Complex Wavelet Transform,” IEEE Transactions on Information Forensics and Security, Vol. 3, No. 3, 2008, pp. 466-474. doi:10.1109/TIFS.2008.927421
[4]	Y. L. Wang and A. Pearmain, “Blind MPEG-2 Video Watermarking Robust against Geometric Attacks: A Set of Approaches in DCT Domain,” IEEE Transactions on Image Processing, Vol. 15, No. 6, 2006, pp. 1536-1543. doi:10.1109/TIP.2006.873476
[5]	X. M. Niu, et al., “A Video Watermarking against Geometrical Distortions,” Chinese Journal of Electronics, Vol. 12, No. 4, 2003, pp. 548-552.
[6]	M. Silja and K. Soman, “A Watermarking Algorithm based on Contourlet Transform and Nonnegative Matrix Factorization,” Proceedings of the International Conference on Advances in Recent Technologies in Communication and Computing, Kottayam, 27-28 October 2009, pp. 279-281. doi:10.1109/ARTCom.2009.198
[7]	A. D’Angelo, Z. P. Li and M. Barni, “A Full-Reference Quality Metric for Geometrically Distorted Images,” IEEE Transactions on Image Processing, Vol. 19, No. 4, 2010, pp. 867-881. doi:10.1109/TIP.2009.2035869
[8]	Z. Y. Yang, et al., “Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization,” IEEE Transactions on Image Processing, Vol. 20, No. 4, 2011, pp. 1112-1125. doi:10.1109/TIP.2010.2081678
[9]	M. Tong, T. Yan and H.-B. Ji, “Strong Anti-Robust WaterMarking Algorithm,” Journal of Xidian University, Vol. 36, No. 1, 2009, pp. 22-27.
[10]	D. Gai, X. F. He, J. W. Han and T. S. Huang, “Graph Regularized Nonnegative Matrix Factorization for Data Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 33, No. 1, 2011, pp. 1-13. doi:10.1109/TPAMI.2010.231
[11]	T. Gao and M.-Y. He, “Using Improved Nonnegative Matrix Factorization with Projected Gradient for Single-Trial Feature Extraction,” Journal of Electronics & Information Technology, Vol. 32, No. 5, 2010, pp. 1121-1125.
[12]	P. O. Hoyer, “Nonnegative Matrix Factorization with Sparseness Constraints,” Journal of Machine Learning Research, Vol. 5, 2004, pp. 1457-1469.
[13]	T. K. Kim, et al., “Video Object Segmentation and Its Salient Motion Detection Using Adaptive Background Generation,” Electronics Letters, Vol. 45, No. 11, 2009, p. 542.
[14]	S.-W. Kim, K. R. Rao, S. Suthaharan and H.-K. Lee, “Perceptually Tuned Robust Watermarking Scheme for Digital Video Using Motion Entropy Masking,” Proceedings of the International Conference on Consumer Electronics of the IEEE ICCE, Los Angeles, 22-24 June 1999, pp. 104-105. doi:10.1109/ICCE.1999.785187