Model complexity control and statisticallearning theory
Natural Computing: an international journal
Digital Video Steganalysis Exploiting Statistical Visibility in the Temporal Domain
IEEE Transactions on Information Forensics and Security
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
Forensic analysis of nonlinear collusion attacks for multimedia fingerprinting
IEEE Transactions on Image Processing
Undergraduate research in computer forensics
Proceedings of the 2011 Information Security Curriculum Development Conference
Moving steganography and steganalysis from the laboratory into the real world
Proceedings of the first ACM workshop on Information hiding and multimedia security
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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.