Towards Generalizable Forgery Detection and Reasoning

• URL: http://arxiv.org/abs/2503.21210v2
• Date: Fri, 15 Aug 2025 03:34:52 GMT
• Title: Towards Generalizable Forgery Detection and Reasoning
• Authors: Yueying Gao, Dongliang Chang, Bingyao Yu, Haotian Qin, Muxi Diao, Lei Chen, Kongming Liang, Zhanyu Ma,
• Abstract summary: We formulate detection and explanation as a unified Forgery Detection and Reasoning task (FDR-Task)<n>We introduce the Multi-Modal Forgery Reasoning dataset (MMFR-Dataset), a large-scale dataset containing 120K images across 10 generative models, with 378K reasoning annotations on forgery attributes.<n>Experiments across multiple generative models demonstrate that FakeReasoning achieves robust generalization and outperforms state-of-the-art methods on both detection and reasoning tasks.
• Score: 23.858913560970866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and interpretable detection of AI-generated images is essential for mitigating risks associated with AI misuse. However, the substantial domain gap among generative models makes it challenging to develop a generalizable forgery detection model. Moreover, since every pixel in an AI-generated image is synthesized, traditional saliency-based forgery explanation methods are not well suited for this task. To address these challenges, we formulate detection and explanation as a unified Forgery Detection and Reasoning task (FDR-Task), leveraging Multi-Modal Large Language Models (MLLMs) to provide accurate detection through reliable reasoning over forgery attributes. To facilitate this task, we introduce the Multi-Modal Forgery Reasoning dataset (MMFR-Dataset), a large-scale dataset containing 120K images across 10 generative models, with 378K reasoning annotations on forgery attributes, enabling comprehensive evaluation of the FDR-Task. Furthermore, we propose FakeReasoning, a forgery detection and reasoning framework with three key components: 1) a dual-branch visual encoder that integrates CLIP and DINO to capture both high-level semantics and low-level artifacts; 2) a Forgery-Aware Feature Fusion Module that leverages DINO's attention maps and cross-attention mechanisms to guide MLLMs toward forgery-related clues; 3) a Classification Probability Mapper that couples language modeling and forgery detection, enhancing overall performance. Experiments across multiple generative models demonstrate that FakeReasoning not only achieves robust generalization but also outperforms state-of-the-art methods on both detection and reasoning tasks.

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