Summary of Fakeshield: Explainable Image Forgery Detection and Localization Via Multi-modal Large Language Models, by Zhipei Xu et al.
FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models
by Zhipei Xu, Xuanyu Zhang, Runyi Li, Zecheng Tang, Qing Huang, Jian Zhang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The rapid growth of generative AI has both benefits and drawbacks, making image manipulation easier but also harder to detect. Current image forgery detection and localization (IFDL) methods are effective but face two challenges: the black-box nature of their detection principles and limited generalization across diverse tampering methods. To address these issues, this paper proposes an explainable IFDL task and designs FakeShield, a multi-modal framework that evaluates image authenticity, generates tampered region masks, and provides a judgment basis based on pixel-level and image-level tampering clues. The framework also incorporates GPT-4o to enhance existing IFDL datasets, creating the Multi-Modal Tamper Description dataset (MMTD-Set) for training FakeShield’s tampering analysis capabilities. Additionally, the paper introduces 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to detect when images have been fake or manipulated. Right now, there are some ways to do this, but they don’t always work well. The problem is that these methods are hard to understand and don’t work well on all kinds of fakes. To fix this, the authors created a new way to detect image manipulation called FakeShield. It looks at an image and can tell if it’s been tampered with, even if the person who did it used different tools or techniques. They also made a special dataset to help train the system to recognize all kinds of fakes. The results show that FakeShield is really good at detecting fake images and can even tell you where in the image the manipulation happened. |
Keywords
» Artificial intelligence » Generalization » Gpt » Multi modal
