Multi-scale context-aware and boundary-guided image manipulation detection method

Abstract

Aiming at the problems of traditional image manipulation detection methods, such as fuzzy boundaries, single scale of extracted features, and ignoring background information, this paper proposes an image manipulation detection method based on multi-scale context-aware and boundary-guided. First, spatial details and base features of manipulated images are extracted using an improved pyramid vision transformer. Second, information related to the edge of the falsified region is explored by an edge contextaware module to generate an edge prediction map. Again, the edge guidance module is utilized to highlight the key channels in the extracted features and reduce the interference of redundant channels. Then, the rich contextual information of the manipulated region is learned from multiple sensory fields through the multi-scale context-aware module. Finally, the feature fusion module is utilized to accurately segment the manipulated region by focusing alternately on the foreground and background of the manipulated images. Comparing this paper's method quantitatively and qualitatively on five commonly used public image manipulation detection datasets, the experimental results show that this paper's method can effectively detect manipulated regions and outperforms other methods.