TRACER: Dual Evidence Modeling for Generalizable Image Forgery Detection

Recent advances in image generation and editing have led to increasingly diverse and realistic image forgeries, making reliable detection more challenging. However, existing forensic methods are typically designed for specific forgery forms and rely on type-specific artifacts, such as boundary and noise inconsistencies in locally manipulated images or structural and frequency irregularities in AI-generated images, making them difficult to extend to other forgery scenarios. To address this limitation, we propose TRACER, a generalizable image forgery detection framework for diverse forgery forms. Our key insight is that forgery processes introduce anomalous deviations from real-image distributions, which can be primarily characterized from two views, fine-grained and logical. Accordingly, TRACER adopts a dual-stream architecture that models each evidence view with a dedicated stream. To promote their collaboration, Bidirectional Evidence Sharing facilitates information exchange between streams, while Hybrid Layer Interaction controls where such exchange occurs across feature depths. Progressive Evidence Training then gradually activates cross-stream interaction. Across benchmarks covering several forgery scenarios, TRACER achieves state-of-the-art performance, improving average evidence localization mIoU by 8.3% and judgment accuracy by 22.5%. It also reaches 92.6% zero-shot accuracy on synthetic images and videos from recent generators. These results show the potential of dual evidence modeling for accurate and generalizable image forgery detection.