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Domain Adaptation for Video Steganalysis Against Motion Vector based Steganography

机译:基于运动向量的隐性杂志的视频隐析域改编

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Video steganalysis takes effect when videos corrupted by the target steganography method are available. Nevertheless, classical classifiers deteriorate in the opposite case. This paper presents a method to cope with the problem of steganography method mismatch for the detection of motion vector (MV) based steganography. Firstly, Adding-or-Subtracting-One (AoSO) feature against MV based steganography and Transfer Component Analysis (TCA) for domain adaptation are revisited. Distributions of AoSO feature against various MV based steganography methods are illustrated, followed by the potential effect of TCA based AoSO feature. Finally, experiments are carried out on various cases of steganography method mismatch. Performance results demonstrate that TCA+AoSO feature significantly outperforms AoSO feature, and is more favorable for real-world applications.
机译:当目标隐写方法损坏的视频可用时,视频隐析生效。然而,古典分类器在相反的情况下恶化。本文介绍了一种方法,以应对基于运动矢量(MV)的隐写法检测的隐写法错配。首先,重新讨论对基于MV的隐识别和传输分析(TCA)添加或减去一个(AOSO)特征进行域适应。示出了AOSO特征对各种基于MV的隐写法方法的分布,然后是基于TCA的AOSO特征的潜在效果。最后,在各种隐喻方法失配的情况下进行实验。性能结果表明,TCA + Aoso功能明显优于Aoso特征,对现实世界的应用更有利。

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