JPEG Compression-aware Image Forgery Localization

Image forgery localization, which aims to find suspicious regions tampered with splicing, copy-move or removal manipulations, has attracted increasing attention. Existing image forgery localization methods have made great progress on public datasets. However, these methods suffer a severe performance drop when the forged images are JPEG compressed, which is widely applied in social media transmission. To tackle this issue, we propose a wavelet-based compression representation learning scheme for the specific JPEG-resistant image forgery localization. Specifically, to improve the performance against JPEG compression, we first learn the abstract representations to distinguish various compression levels through wavelet integrated contrastive learning strategy. Then, based on the learned representations, we introduce a JPEG compression-aware image forgery localization network to flexibly handle forged images compressed with various JPEG quality factors. Moreover, a boundary correction branch is designed to alleviate the edge artifacts caused by JPEG compression. Extensive experiments demonstrate the superiority of our method to existing state-of-the-art approaches, not only on standard datasets, but also on the JPEG forged images with multiple compression quality factors.