FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models (2410.02761v4)

Abstract: 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.

• The paper introduces FakeShield, a novel framework leveraging multi-modal large language models for explainable image forgery detection and localization.
• Experimental results show FakeShield achieves superior detection accuracy and localization capability compared to existing state-of-the-art models on various datasets.
• FakeShield provides detailed, explainable insights into image tampering, advancing forensic analysis and enabling better generalization across diverse forgery techniques.

An Overview of "FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal LLMs"

In the paper titled "FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal LLMs," the authors introduce a novel framework, FakeShield, to address the challenges associated with image forgery detection and localization (IFDL). The paper emphasizes the importance of transparency and generalization in forgery detection systems, focusing on overcoming the limitations posed by existing methods, which often function as black-box models and struggle with various tampering techniques.

Methodology and Contributions

The FakeShield framework is designed to provide explainable forgery detection and localization using a multi-modal approach. It leverages the capabilities of LLMs to interpret and analyze images. The proposed system consists of two key components: the Domain Tag-guided Explainable Forgery Detection Module (DTE-FDM) and the Multi-modal Forgery Localization Module (MFLM).

1. Domain Tag-guided Explainable Forgery Detection Module (DTE-FDM): This module centralizes on distinguishing different types of forgery by generating a domain tag that guides the model in identifying the forgery type, whether it be Photoshop-based, DeepFake, or AIGC-Editing. This approach notably enhances the model's generalization capabilities across diverse tampering techniques.
2. Multi-modal Forgery Localization Module (MFLM): This module is responsible for accurately localizing the areas within an image that have been tampered with. By integrating the Segment Anything Model (SAM) with a Tamper Comprehension Module (TCM), the system aligns visual and textual representations to pinpoint manipulation areas accurately.

The authors also introduce the Multi-Modal Tamper Description dataSet (MMTD-Set), which enhances existing IFDL datasets through the use of GPT-4o to generate detailed descriptions of tampered areas. This dataset plays a critical role in training the proposed FakeShield framework, allowing it to detect and localize forgeries more precisely.

Experimental Results

The paper presents extensive experimental evaluations of FakeShield against existing state-of-the-art IFDL models, revealing several significant findings:

• Detection Accuracy: On various datasets, including CASIA1+, DeepFake, and AIGC-Editing, FakeShield achieved superior detection accuracy, outperforming competitors with significantly higher accuracy and F1 scores.
• Localization Capability: In localization tasks, FakeShield consistently provided better IoU and F1 scores, demonstrating its ability to accurately identify tampered regions in images.
• Explainability: Through the use of cosine semantic similarity (CSS) as a metric, FakeShield's capability to offer detailed tampering explanations was notably better than other pre-trained multi-modal LLMs.

Implications and Future Work

The research offers practical advancements in forensic analysis, where the demand for reliable and explainable IFDL systems continues to rise due to the increasing proliferation of image manipulation technologies. The use of domain tags to guide detection provides pathways for future work in enhancing model explainability and adaptability to new forms of tampering. Additionally, the approach of using LLMs to generate comprehensive, interpretable results could inspire further integration of multi-modal models across various AI disciplines.

In conclusion, FakeShield represents a significant leap forward in the field of explainable IFDL, addressing key challenges by combining the interpretative power of LLMs with robust detection and localization capabilities. While there is room for further enhancements—particularly in handling more complex deepfake scenarios—the framework sets a promising foundation for future research in AI-driven image forensics.