Double H.264 compression detection scheme based on prediction residual of background regions

Abstract

Detection of double video compression plays an important role in video forensics. However, existing methods rarely focused on H.264 videos and are unreliable to provide detection results for static-background videos with fast moving foregrounds. In this paper, an effective double compression detection scheme based on Prediction Residual of Background Regions (PRBR) is proposed to overcome these limitations. Firstly, the mask of background regions in each frame is obtained by applying Visual Background Extractor (VIBE). VIBE is an efficient and robust background subtraction algorithm, which can distinguish the background and foreground of each frame at pixel level. Then, the PRBR feature is designed to characterize the statistical distribution of average prediction residual within the background mask. After that, the Jesen-Shannon Divergence is introduced to measure the difference between the PRBR features of the adjacent two frames. Finally, a periodic analysis method is applied to the final feature sequence for double H.264 compression detection and estimation of the first Group Of Pictures (GOP). Eighteen standard testing sequences captured by fixed cameras are used to establish the double compression dataset. Experiments demonstrate the proposed scheme can achieve better performance compared the-state-of-art methods.