A video steganalysis method based on coding cost variation

Aiming at the problems existing in existing steganalysis algorithms, this article proposes Motion Vector Coding Cost Change video steganalysis features based on Improved Motion Vector Reversion-Based features and Subtractive Probability of Coding Cost Optimal Matching features based on Subtractive Probability of Optimal Matching features from the perspective of the change of coding cost. Motion Vector Coding Cost Change features can be well consistent with the coding cost before recoding by analyzing the sub-pixel coding cost of recoding. By counting the sub-pixel coding costs of motion vectors before and after video recoding, the Sum of Absolute Difference values of motion vectors instead of predicted residuals are applied to steganalysis and detection, and the steganographic algorithm based on motion vectors is effectively detected. Experiments show that Motion Vector Coding Cost Change features have higher detection accuracy than Add-or-Subtract-One, Improved Motion Vector Reversion-Based, and other typical features in various steganography methods, and Subtractive Probability of Coding Cost Optimal Matching features have higher detection effect and better robustness than Subtractive Probability of Optimal Matching features.

[1]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[2]  Jessica J. Fridrich,et al.  Rich Models for Steganalysis of Digital Images , 2012, IEEE Transactions on Information Forensics and Security.

[3]  Dengguo Feng,et al.  Video Steganalysis Exploiting Motion Vector Reversion-Based Features , 2012, IEEE Signal Processing Letters.

[4]  Xianfeng Zhao,et al.  An adaptive detecting strategy against motion vector-based steganography , 2015, 2015 IEEE International Conference on Multimedia and Expo (ICME).

[5]  Hong Zhao,et al.  Video Steganalysis Against Motion Vector-Based Steganography by Adding or Subtracting One Motion Vector Value , 2014, IEEE Transactions on Information Forensics and Security.

[6]  Hussein A. Aly,et al.  Data Hiding in Motion Vectors of Compressed Video Based on Their Associated Prediction Error , 2011, IEEE Transactions on Information Forensics and Security.

[7]  Jessica J. Fridrich,et al.  Low-Complexity Features for JPEG Steganalysis Using Undecimated DCT , 2015, IEEE Transactions on Information Forensics and Security.

[8]  Tao Zhang,et al.  Steganography in Compressed Video Stream , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[9]  Sakir Sezer,et al.  Spatio-temporal rich model for motion vector steganalysis , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Hong Zhang,et al.  Video Steganalysis Based on Intra Prediction Mode Calibration , 2015, IWDW.

[11]  Yuting Su,et al.  A video steganalytic algorithm against motion-vector-based steganography , 2011, Signal Process..

[12]  Lina Wang,et al.  Video steganalysis based on subtractive probability of optimal matching feature , 2014, IH&MMSec '14.

[13]  Xianfeng Zhao,et al.  A Steganalytic Algorithm to Detect DCT-based Data Hiding Methods for H.264/AVC Videos , 2017, IH&MMSec.

[14]  Hong Zhang,et al.  Video Steganography Based on Optimized Motion Estimation Perturbation , 2015, IH&MMSec.

[15]  Hui Ye,et al.  Motion vector-based video steganalysis using spatial-temporal correlation , 2013, 2013 6th International Congress on Image and Signal Processing (CISP).

[16]  Jessica J. Fridrich,et al.  Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes , 2011, IEEE Transactions on Information Forensics and Security.