Video steganography has the potential to be used to convey illegal information, and video steganalysis is a vital tool to detect the presence of this illicit act. Currently, all the motion vector (MV)-based video steganalysis algorithms extract feature sets directly on the MVs, but ignoring the steganograhic operation may perturb the statistics distribution of other video encoding elements, such as the skipped macroblocks (no direct MVs). This paper proposes a novel 11-dimensional feature set to detect MV-based video steganography based on the above observation. The proposed feature is extracted based on the skipped macroblocks by recompression calibration. Specifically, the feature consists of two components. The first is the probability distribution of motion vector prediction (MVP) difference, and the second is the probability distribution of partition state transfer. Extensive experiments on different conditions demonstrate that the proposed feature set achieves good detection accuracy, especially in lower embedding capacity. In addition, the loss of detection performance caused by recompression calibration using mismatched quantization parameters (QP) is within the acceptable range, so the proposed method can be used in practical scenarios.

翻译：视频隐写技术可能被用于传递非法信息，而视频隐写分析是检测此类非法行为的重要手段。当前，所有基于运动矢量的视频隐写分析算法均直接提取MV特征集，却忽略了隐写操作可能扰动其他视频编码元素（如无直接MV的跳过宏块）的统计分布。基于上述发现，本文提出一种新型11维特征集用于检测基于MV的视频隐写。所提特征通过重压缩校准从跳过宏块中提取，具体包含两个分量：运动矢量预测差值概率分布与划分状态转移概率分布。不同条件下的广泛实验表明，该特征集在低嵌入容量场景下尤其具有良好的检测精度。此外，使用不匹配量化参数进行重压缩校准导致的检测性能损失处于可接受范围，因此该方法可应用于实际场景。