An adaptive detecting strategy against motion vector-based steganography

The goal of this paper is to improve the performance of the current video steganalysis in detecting motion vector (MV)-based steganography. It is noticed that many MV-based approaches embed secret bits in content adaptive manners. Typically, the modifications are applied only to qualified MVs, which implies that the number of modified MVs varies among frames after embedding. On the other hand, nearly all the current steganalytic methods ignore such uneven distribution. They divide the video into frame groups equally and calculate every single feature vector using all MVs within one group. For better classification performances, we suggest performing steganalysis also in an adaptive way. First, divide the video into groups with variable lengths according to frame dynamics. Then within each group, calculate a single feature vector using all suspicious MVs (MVs that are likely to be modified). The experimental results have shown the effectiveness of our proposed strategy.