A Novel Approach for Detection of Motion Vector-Based Video Steganography by AoSO Motion Vector Value

Abstract

Although tremendous progress has been made on steganography in last decade but still there exist problems to detect the steganalysis in motion-based video where the content is consistently is in motion behavior which creates hurdles to detect it. The motion value plays a crucial role in analyzing the difference between the rated, which allows us to focus on the difference between the actual SAD and the locally optimal SAD after the adding or subtracting one operation on the motion value. Finally, to perform the classification and extraction process-based motion vectors, two feature sets are been used to complete this process based on the fact that most motion vectors are locally optimal for most video codecs. The proposed method succeeds to meet the application requirement and simultaneously succeed in detecting the steganalysis in videos compared to conventional approaches reported in the literature.