BiRNet: Bilateral-Attentive Refinement Network for Tampering Detection Across Manipulation Paradigms

Image tampering detection faces persistent challenges in localizing manipulation boundaries across diverse forgery techniques, from traditional manipulation to AI-driven diffusion-based synthesis. Both manipulation types exhibit subtle boundary inconsistencies that demand robust feature extraction. However, existing attention mechanisms widely adopted in tampering detection suffer from limitations. Spatial attention produces diluted activations on weak traces, while channel attention amplifies noise from heterogeneous forensic patterns. To refine these widely-used attention mechanisms, we propose BiRNet, a Bilateral-Attentive Refinement Network featuring two key innovations: (1) a Bilateral Cross-Attention Module that enables bidirectional interaction between complementary features with multi-level fusion, and (2) an Attentive Refinement Module that enhances discrimination through parallel local-global pathways. We conduct extensive experiments across seven benchmarks spanning traditional datasets and diffusion-based manipulations. BiRNet achieves an average F1-score of 0.618 across all datasets. These results validate strong cross-domain generalization from conventional to AI-generated forgeries without requiring AIGC-specific training.