Video steganalysis using motion estimation

In this paper we present a novel video steganalysis method using neural networks and support vector machines to detect video steganograms with very limited a-prior knowledge about the steganogram embedding method. We apply temporal and spacial redundancies by using the concept of motion estimation widely used in video compression to every frame to obtain an estimate of the frame and extract the merged Discrete Cosine Features (DCT) and Markov features. MSU stegovideo tool by Moscow State University and the spread spectrum steganography tool are used for producing video steganograms. Results show that the features we use give the best accuracy to detect video steganograms. Our results thus demonstrate the potential of using learning machines and motion estimation in detecting video steganograms.