Generalizable Deep Video Inpainting Detection Based on Constrained Convolutional Neural Networks

Jinchuan Li, Xianfeng Zhao, Yun Cao

Abstract: Deep video inpainting can automatically fill in missing content both in spatial and temporal domain. Unfortunately, malicious video inpainting operations can distort media content, making it challenging for viewers to detect inpainting traces due to their realistic visual effects. As a result, the detection of video inpainting has emerged as a crucial research area in video forensics. Several detection models that have been proposed are trained and tested on datasets made by three kinds of inpainting models, but not tested against the latest and better deep inpainting models. To address this, we introduce a novel end-to-end video inpainting detection network, comprising a feature extraction module and a feature learning module. The Feature extraction module is a Bayar layer and the feature learning module is an encoder-decoder module. The proposed approach is evaluated with inpainted videos created by several state-of-the-art deep video inpainting networks. Extensive experiments has proven that our approach achieved better inpainting localization performance than other methods.