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FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models

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arxiv 2410.02761 v4 pith:KCXZCFCQ submitted 2024-10-03 cs.CV cs.AI

Zhipei Xu , Xuanyu Zhang , Runyi Li , Zecheng Tang , Qing Huang , Jian Zhang This is my paper · ORCID
classification cs.CVcs.AI
keywords detectionfakeshieldforgeryexplainableifdlimagelocalizationmulti-modal
verification ladder T0 review T1 audit T2 compute T3 formal
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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. The code is available at https://github.com/zhipeixu/FakeShield.

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