Contour-assistance-based video matting localization

Abstract

Video matting is a technique used to replace foreground objects in video frames by predicting their alpha matte. Originally developed for film special effects, advertisements, and live streaming, video matting can also be exploited for malicious tampering, leaving imperceptible traces. This highlights the need for effective forensic techniques to detect such tampering. Current research in video matting forensics is limited, largely focusing on frame-by-frame analysis, which fails to account for the temporal characteristics of videos and thus falls short in accurately localizing tampered regions. In this paper, we address this gap by leveraging the entire video sequence to improve tampering detection. We propose a two-branch network that integrates contour information of tampered objects into the forgery localization process, enhancing the extraction of tampering traces and contour features. Additionally, we introduce a tamper contour detection module and a feature enhancement module to refine tampered region identification. Extensive experiments conducted on both overt and synthetic tampering datasets demonstrate that our method effectively locates tampered regions, outperforming existing video forensics techniques.