The rapid development of generative AI is a double-edged sword, which not only facilitates content creation but also makes image manipulation easier and more difficult to detect. Although current image forgery detection and localization (IFDL) methods are generally effective, they tend to face two challenges: \textbf{1)} black-box nature with unknown detection principle, \textbf{2)} limited generalization across diverse tampering methods (e.g., Photoshop, DeepFake, AIGC-Editing). To address these issues, we propose the explainable IFDL task and design FakeShield, a multi-modal framework capable of evaluating image authenticity, generating tampered region masks, and providing a judgment basis based on pixel-level and image-level tampering clues. Additionally, we leverage GPT-4o to enhance existing IFDL datasets, creating the Multi-Modal Tamper Description dataSet (MMTD-Set) for training FakeShield's tampering analysis capabilities. Meanwhile, we incorporate a Domain Tag-guided Explainable Forgery Detection Module (DTE-FDM) and a Multi-modal Forgery Localization Module (MFLM) to address various types of tamper detection interpretation and achieve forgery localization guided by detailed textual descriptions. Extensive experiments demonstrate that FakeShield effectively detects and localizes various tampering techniques, offering an explainable and superior solution compared to previous IFDL methods.

翻译：生成式人工智能的快速发展是一把双刃剑，它不仅促进了内容创作，也使图像篡改变得更加容易且难以检测。尽管当前的图像伪造检测与定位方法普遍有效，但它们往往面临两个挑战：**1)** 检测原理未知的黑盒特性，**2)** 对不同篡改方法（如Photoshop、DeepFake、AIGC编辑）的泛化能力有限。为解决这些问题，我们提出了可解释的IFDL任务，并设计了FakeShield——一个能够评估图像真实性、生成篡改区域掩码、并基于像素级和图像级篡改线索提供判断依据的多模态框架。此外，我们利用GPT-4o对现有IFDL数据集进行增强，构建了多模态篡改描述数据集，用于训练FakeShield的篡改分析能力。同时，我们引入了域标签引导的可解释伪造检测模块和多模态伪造定位模块，以处理各类篡改检测的解释需求，并实现基于详细文本描述的伪造定位。大量实验表明，FakeShield能有效检测并定位多种篡改技术，相比以往的IFDL方法，提供了一种可解释且性能更优的解决方案。