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.
Abstract (translated)
图像篡改技术的进步带来了严重的安全威胁，突显了有效图像操作定位（IML）的必要性。虽然监督下的IML取得了很好的性能，但它依赖于昂贵的像素级标注。现有的弱监督或无训练替代方法往往表现不佳且缺乏解释能力。我们提出了一个无训练框架——上下文取证链（ICFC），该框架利用多模态大型语言模型（MLLM）来进行可解释的图像操作定位任务。 ICFC结合了对象化规则构建和自适应过滤，建立了一个可靠的知识库，并形成一个多步骤渐进推理管道，这一设计模仿了专家级的取证工作流程，从粗略提案到细粒度的取证结果。这种设计能够系统地利用MLLM推理进行图像级别的分类、像素级别的定位以及文本级别的解释。 在多个基准测试中，ICFC不仅超越了现有的无训练方法，而且在性能上与弱监督和全监督方法相当或更优。
URL
https://arxiv.org/abs/2510.10111
