EC-Net: General image tampering localization network based on edge distribution guidance and contrastive learning

Abstract

General Image Tampering Localization (GITL) is a challenging task that aims to locate tampered regions in manipulated images accurately. Despite notable achievements in recent years, existing methods still struggle with generalization and pixel-level segmentation, particularly in the face of diverse tampering forms and invisible traces. To address these issues, we propose EC-Net, a GITL network based on edge distribution guidance and contrastive learning. EC-Net decomposes the task into two phases: the coarse locating phase and the fine locating phase. In the coarse locating phase, a contrastive learning strategy is incorporated to improve the model's generalization capabilities. Additionally, an edge distribution prediction method is introduced to anticipate the global contour information of tampered regions. In the fine locating phase, we introduced a cross-scale fusion mechanism to synthesize feature information across different scales, enhancing the model's ability to discern subtle changes in tampered regions. Additionally, an edge reconstruction mechanism is introduced to restore details, thereby improving the accuracy of the final localization results. Evaluation on seven datasets shows that EC-Net significantly outperforms seven state-of-the-art methods, improving localization performance by 0.095 (F1), 0.088 (IoU), and 0.043 (AUC) compared to the second-best method on average. Additionally, we present a tampered image dataset (RLS26K) for model auxiliary training, featuring diverse real-life scenes with various tampering types, diverse styles, and rich content.