MB-Net: multiscale boundary interaction learning for image manipulation localization

Image editing techniques can modify the content of images indiscriminately, which causes a grave threat to the security of society. Hence, the localization of manipulated images is inevitable. A serious challenge for image manipulation detection is the lack of strategies for perceiving global features and refining edges. In this paper, we present a multiscale boundary interaction learning network for image manipulation localization to solve both problems. This network contains an adjacent-scale mutual module to enrich the global perception domain by interactively learning adjacent scale features. It avoids the tremendous noise interference caused by the direct fusion of all scale features. To effectively suppress semantic content segmentation, the boundary pixel disparity module computes interpixel differences at specific angles to enhance boundary artifact recognition between tampered and real regions. The fusion attention module is proposed to combine scale and edge messages, integrating spatial and channel correlations in a compatible way. Extensive experimental results indicate that our proposed method is significantly superior to current state-of-the-art methods on public standard datasets.