CDS-Net: Cooperative dual-stream network for image manipulation detection

To accurately locate manipulated regions, many existing approaches employ a dual-stream framework to extract a wide range of manipulation clues, including local noise, edge artifacts, and global inconsistency. However, these approaches treat each stream in isolation and fail to consider the complementary and mutual guidance ability between the streams. Moreover, we notice the use of vanilla vision transformers in previous approaches can result in disruptions of object semantics, causing incomplete predictions. To address these challenges, we introduce the cooperative dual-stream network (CDS-Net) comprising an RGB Stream and a Noise Stream. In the Noise Stream, we propose a K-means Transformer (KT) that encourages both inter-patch and intra-patch information transmission to mitigate the semantic fragmentation phenomenon caused by patch partitioning. Additionally, we introduce a novel Feature Interaction Block (FIB) that explicitly encourages cross-stream collaboration at each encoding stage. Comprehensive experiments on publicly available datasets demonstrate the effectiveness and robustness of CDS-Net.