ForRAIL: Forensic Retrieval-Augmented Visual In-Context Learning for Training-Free Image Forgery Localization

Image forgery localization (IFL) has become increasingly challenging as modern image editing models produce realistic and semantically coherent manipulations, while existing methods often rely on task-specific training and struggle to generalize to unseen manipulation techniques and generators. Multimodal large language models (MLLMs) offer broad visual reasoning capabilities and cross-domain knowledge, yet their potential for precise, training-free forgery localization remains largely unexplored. In this work, we investigate whether a frozen MLLM can perform pixel-level IFL through visual in-context learning. We propose Retrieval-Augmented Visual In-Context Learning for IFL (ForRAIL), a training-free framework that retrieves informative forgery demonstrations for each query to guide localization without updating model parameters. To improve demonstration selection, we further introduce CoSFoR, a coarse-to-fine semantic–forgery retrieval strategy that jointly considers semantic relevance and manipulation-sensitive characteristics. Extensive experiments across diverse out-of-domain datasets, generators, and multiple MLLM backbones show that ForRAIL substantially outperforms vanilla MLLMs and existing training-free MLLM-based approaches and exceeds the best specialized trained forensic models 0.03 IoU on OOD average. It also maintains strong localization performance across different tampered-region sizes and under common image perturbations, including Gaussian blur, Gaussian noise, JPEG compression, and scale. These results establish retrieval-augmented visual in-context learning as a promising training-free paradigm for generalizable image forgery localization.