Motion vector‐domain video steganalysis exploiting skipped macroblocks

Abstract

Abstract 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 from the MVs, but ignoring the embedding operation may perturb the statistical 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 capacities. 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.

Keywords

• digital forensics
• steganography
• video signal processing