Peipei Wang
ABSTRACT
This paper presents an effective motion vector (MV)-based steganography to cope with different steganalytic models. The main principle is to define a distortion scale expressing the multi-level embedding impact of MV modification. Three factors including motion characteristic of video content, MV's local optimality and statistical distribution are considered in distortion definition. For every embedding location, the contributions of three factors are dynamically adjusted according to MV's property. Based on the defined distortion function, two layered syndrome-trellis codes (STCs) are utilized to minimize the overall embedding impact in practical embedding implementation. Experimental results demonstrate that the proposed method achieves higher level of security compared with other existing MV-based approaches, especially for high quality videos.
- F. JORDAN. Proposal of a watermarking technique for hiding/retrieving data in compressed and decompressed video. ISO/IEC Doc. JTC1/SC 29/QWG 11 MPEG 97/M 2281, 1997.Google Scholar
- Changyong Xu, Xijian Ping, and Tao Zhang. Steganography in compressed video stream. In Innovative Computing, Information and Control, 2006. ICICIC '06. First International Conference on, volume 1, pages 269--272, Aug 2006. Google ScholarDigital Library
- H.A. Aly. Data hiding in motion vectors of compressed video based on their associated prediction error. Information Forensics and Security, IEEE Transactions on, 6(1):14--18, March 2011. Google ScholarDigital Library
- J. Fridrich, M. Goljan, P. Lisonek, and D. Soukal. Writing on wet paper. Signal Processing, IEEE Transactions on, 53(10):3923--3935, Oct 2005. Google ScholarDigital Library
- Tomáš Filler, Jan Judas, and Jessica Fridrich. Minimizing embedding impact in steganography using trellis-coded quantization, 2010.Google Scholar
- T. Filler, J. Judas, and J. Fridrich. Minimizing additive distortion in steganography using syndrome-trellis codes. Information Forensics and Security, IEEE Transactions on, 6(3):920--935, Sept 2011. Google ScholarDigital Library
- Yun Cao, Xianfeng Zhao, Dengguo Feng, and Rennong Sheng. Information Hiding: 13th International Conference, IH 2011, Prague, Czech Republic, May 18--20, 2011, Revised Selected Papers, chapter Video Steganography with Perturbed Motion Estimation, pages 193--207. Springer Berlin Heidelberg, Berlin, Heidelberg, 2011. Google ScholarDigital Library
- Yuanzhi Yao, Weiming Zhang, Nenghai Yu, and Xianfeng Zhao. Defining embedding distortion for motion vector-based video steganography. Multimedia Tools and Applications, 74(24):11163--11186, 2014. Google ScholarDigital Library
- Yanzhen Ren, Liming Zhai, Lina Wang, and Tingting Zhu. Video steganalysis based on subtractive probability of optimal matching feature. In Proceedings of the 2Nd ACM Workshop on Information Hiding and Multimedia Security, IH&MMSec '14, pages 83--90, New York, NY, USA, 2014. ACM. Google ScholarDigital Library
- Keren Wang, Hong Zhao, and Hongxia Wang. Video steganalysis against motion vector-based steganography by adding or subtracting one motion vector value. Information Forensics and Security, IEEE Transactions on, 9(5):741--751, May 2014. Google ScholarDigital Library
- Hong Zhang, Yun Cao, and Xianfeng Zhao. Motion vector-based video steganography with preserved local optimality. Multimedia Tools and Applications, pages 1--17, 2015.Google Scholar
- Yun Cao, Hong Zhang, Xianfeng Zhao, and Haibo Yu. Video steganography based on optimized motion estimation perturbation. In Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security, IH&MMSec '15, pages 25--31, New York, NY, USA, 2015. ACM. Google ScholarDigital Library
- Yun Cao, Xianfeng Zhao, and Dengguo Feng. Video steganalysis exploiting motion vector reversion-based features. Signal Processing Letters, IEEE, 19(1):35--38, Jan 2012.Google ScholarCross Ref
- Peipei Wang, Yun Cao, Xianfeng Zhao, and Bin Wu. Motion vector reversion-based steganalysis revisited. In Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, pages 463--467, July 2015.Google ScholarCross Ref
- Tomáš Pevnỳ, Tomáš Filler, and Patrick Bas. Information Hiding: 12th International Conference, IH 2010, Calgary, AB, Canada, June 28--30, 2010, Revised Selected Papers, chapter Using High-Dimensional Image Models to Perform Highly Undetectable Steganography, pages 161--177. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010. Google ScholarDigital Library
- V. Holub and J. Fridrich. Designing steganographic distortion using directional filters. In Information Forensics and Security (WIFS), 2012 IEEE International Workshop on, pages 234--239, Dec 2012.Google ScholarCross Ref
- Danfeng Xie, Zhiwei Huang, Shizheng Wang, and Heguang Liu. Moving objects segmentation from compressed surveillance video based on motion estimation. In Pattern Recognition (ICPR), 2012 21st International Conference on, pages 3132--3135, Nov 2012.Google Scholar
- Wei Zeng, Jun Du, Wen Gao, and Qingming Huang. Robust moving object segmentation on h.264/avc compressed video using the block-basedMRF\ model. Real-Time Imaging, 11(4):290 -- 299, 2005. Google ScholarDigital Library
- R. Venkatesh Babu and K.R. Ramakrishnan. Recognition of human actions using motion history information extracted from the compressed video. Image and Vision Computing, 22(8):597 -- 607, 2004.Google ScholarCross Ref
- T. Wiegand, G.J. Sullivan, G. Bjontegaard, and A. Luthra. Overview of the h.264/avc video coding standard. Circuits and Systems for Video Technology, IEEE Transactions on, 13(7):560--576, July 2003. Google ScholarDigital Library
- M. Tom and R.V. Babu. Fast moving-object detection in h.264/avc compressed domain for video surveillance. In Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013 Fourth National Conference on, pages 1--4, Dec 2013.Google Scholar
- Yuting Su, Chengqian Zhang, and Chuntian Zhang. A video steganalytic algorithm against motion-vector-based steganography. Signal Processing, 91(8):1901--1909, 2011. Google ScholarDigital Library
- VideoLan. x264. Available: http://www.videolan.org/developers/x264.html.Google Scholar
- C.Chang and C.Lin. LIBSVM: A Library for Support Vector Machines, 2001 {online}. Available: http://www.csie.ntu.edu.tw/cjlin/libsvm. Google ScholarDigital Library
Index Terms
- A Novel Embedding Distortion for Motion Vector-Based Steganography Considering Motion Characteristic, Local Optimality and Statistical Distribution
Recommendations
Video Steganography Based on Optimized Motion Estimation Perturbation
IH&MMSec '15: Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia SecurityIn this paper, a novel motion vector-based video steganographic scheme is proposed, which is capable of withstanding the current best statistical detection method. With this scheme, secret message bits are embedded into motion vector (MV) values by ...
Defining embedding distortion for motion vector-based video steganography
This paper presents an effective methodology for motion vector-based video steganography. The main principle is to design a suitable distortion function expressing the embedding impact on motion vectors by exploiting the spatial-temporal correlation ...
Undetectable video steganography by considering spatio-temporal steganalytic features in the embedding cost function
AbstractThe basic requirement of a steganography approach is security against steganalysis attacks. In other words, a steganography method is reliable as long as it withstands all of the known steganalysis approaches. In order to preserve the security of ...
Comments