Automatic Detection of Object-Based Forgery in Advanced Video

Shengda Chen, Shunquan Tan, Bin Li, Jiwu Huang

Abstract: Passive multimedia forensics has become an active topic in recent years. However, less attention has been paid to video forensics. Research on video forensics, and especially on automatic detection of object-based video forgery, is still in its infancy. In this paper, we develop an approach for automatic identification and forged segment localization of object-based forged video encoded with advanced frameworks. The proposed approach starts with a frame manipulation detector. An automatic algorithm is proposed to identify object-based video forgery based on the frame manipulation detector. Then, a two-stage automatic algorithm is provided to accurately locate the forged video segments in the suspicious video. To construct the proposed frame manipulation detector, motion residuals are generated from the target video frame sequence. We regard the object-based forgery in video frames as image tampering in the motion residuals and employ the feature extractors that are originally built for still image steganalysis to extract forensic features from the motion residuals. The experiments show that the proposed approach achieves excellent results in both forged video identification and automatic forged temporal segment localization.