Training-Free In-Context Forensic Chain for Image Manipulation Detection and Localization

• URL: http://arxiv.org/abs/2510.10111v2
• Date: Mon, 27 Oct 2025 11:57:33 GMT
• Title: Training-Free In-Context Forensic Chain for Image Manipulation Detection and Localization
• Authors: Rui Chen, Bin Liu, Changtao Miao, Xinghao Wang, Yi Li, Tao Gong, Qi Chu, Nenghai Yu,
• Abstract summary: In-Context Forensic Chain (ICFC) is a training-free framework that leverages multi-modal large language models (MLLMs) for interpretable IML tasks.<n>ICFC integrates an objectified rule construction with adaptive filtering to build a reliable knowledge base.<n>ICFC not only surpasses state-of-the-art training-free methods but also achieves competitive or superior performance compared to weakly and fully supervised approaches.
• Score: 49.551943094262164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advances in image tampering pose serious security threats, underscoring the need for effective image manipulation localization (IML). While supervised IML achieves strong performance, it depends on costly pixel-level annotations. Existing weakly supervised or training-free alternatives often underperform and lack interpretability. We propose the In-Context Forensic Chain (ICFC), a training-free framework that leverages multi-modal large language models (MLLMs) for interpretable IML tasks. ICFC integrates an objectified rule construction with adaptive filtering to build a reliable knowledge base and a multi-step progressive reasoning pipeline that mirrors expert forensic workflows from coarse proposals to fine-grained forensics results. This design enables systematic exploitation of MLLM reasoning for image-level classification, pixel-level localization, and text-level interpretability. Across multiple benchmarks, ICFC not only surpasses state-of-the-art training-free methods but also achieves competitive or superior performance compared to weakly and fully supervised approaches.

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