Video Steganalysis Exploiting Motion Vector Reversion-Based Features

Abstract

Unlike traditional image or video steganography in spatial/transform domain, motion vector (MV)-based methods target the internal dynamics of video compression and embed messages while performing motion estimation. However, we have noticed that some existing methods adopt nonoptimal selection rules and modify MVs in somewhat arbitrary manners which violate the encoding principles a lot. Aiming at these weaknesses, we design a calibration-based approach and propose MV reversion-based features for steganalysis. Experimental results demonstrate that the proposed features are very sensitive to the tendency of MV reversion during calibration and can be used to effectively detect some typical MV-based steganography even with low embedding rates.