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A video steganalysis method based on coding cost variation
International Journal of Distributed Sensor Networks ( IF 2.3 ) Pub Date : 2021-02-04 , DOI: 10.1177/1550147721992730
Jianyi Liu 1 , Cong Zhang 1 , Ru Zhang 1 , Yi Li 1 , Jie Cheng 2
Affiliation  

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.



中文翻译:

基于编码成本变化的视频隐写分析方法

针对现有隐写分析算法中存在的问题,从视角出发,提出了基于改进的基于运动矢量回归的特征的运动矢量编码成本变化视频隐写特征和基于最优匹配特征的减法概率的编码成本最优匹配特征的减法概率。编码成本的变化。通过分析重新编码的子像素编码成本,运动矢量编码成本更改功能可以很好地与重新编码之前的编码成本保持一致。通过计算视频编码前后运动矢量的亚像素编码成本,将运动矢量的绝对差值之和而不是预测的残差应用于隐写分析和检测,从而有效地检测了基于运动矢量的隐写算法。

更新日期:2021-02-05
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