Towards digital video steganalysis using asymptotic memoryless detection
Proceedings of the 9th workshop on Multimedia & security
Detection of motion-incoherent components in video streams
IEEE Transactions on Information Forensics and Security
Digital video watermarking in 3-D ridgelet domain
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 3
Video steganalysis using motion estimation
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Complete video quality-preserving data hiding
IEEE Transactions on Circuits and Systems for Video Technology
A review of the audio and video steganalysis algorithms
Proceedings of the 48th Annual Southeast Regional Conference
A video steganalytic algorithm against motion-vector-based steganography
Signal Processing
Video steganography with perturbed motion estimation
IH'11 Proceedings of the 13th international conference on Information hiding
IH'11 Proceedings of the 13th international conference on Information hiding
Moving steganography and steganalysis from the laboratory into the real world
Proceedings of the first ACM workshop on Information hiding and multimedia security
A practical design of high-volume steganography in digital video files
Multimedia Tools and Applications
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In this paper, we present effective steganalysis techniques for digital video sequences based on interframe collusion that exploits the temporal statistical visibility of a hidden message. Steganalysis is the process of detecting, with high probability, the presence of covert data in multimedia. Present image steganalysis algorithms when applied directly to video sequences on a frame-by-frame basis are suboptimal; we present methods that overcome this limitation by using redundant information present in the temporal domain to detect covert messages embedded via spread-spectrum steganography. Our performance gains are achieved by exploiting the collusion attack that has recently been studied in the field of digital video watermarking and pattern recognition tools. Through analysis and simulations, we evaluate the effectiveness of the video steganalysis based on linear collusion approaches. The proposed steganalysis methods are successful in detecting hidden watermarks bearing low energy with high accuracy. The simulation results also show the improved performance of the proposed temporal-based methods over purely spatial methods