Training-Free Multimodal Deepfake Detection via Graph Reasoning

• URL: http://arxiv.org/abs/2509.21774v1
• Date: Fri, 26 Sep 2025 02:22:12 GMT
• Title: Training-Free Multimodal Deepfake Detection via Graph Reasoning
• Authors: Yuxin Liu, Fei Wang, Kun Li, Yiqi Nie, Junjie Chen, Yanyan Wei, Zhangling Duan, Zhaohong Jia,
• Abstract summary: Multimodal deepfake detection (MDD) aims to uncover manipulations across visual, textual, and auditory modalities.<n>We propose Guided Adaptive Scorer and Propagation In-Context Learning (GASP-ICL), a training-free framework for MDD.
• Score: 16.774618707890834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal deepfake detection (MDD) aims to uncover manipulations across visual, textual, and auditory modalities, thereby reinforcing the reliability of modern information systems. Although large vision-language models (LVLMs) exhibit strong multimodal reasoning, their effectiveness in MDD is limited by challenges in capturing subtle forgery cues, resolving cross-modal inconsistencies, and performing task-aligned retrieval. To this end, we propose Guided Adaptive Scorer and Propagation In-Context Learning (GASP-ICL), a training-free framework for MDD. GASP-ICL employs a pipeline to preserve semantic relevance while injecting task-aware knowledge into LVLMs. We leverage an MDD-adapted feature extractor to retrieve aligned image-text pairs and build a candidate set. We further design the Graph-Structured Taylor Adaptive Scorer (GSTAS) to capture cross-sample relations and propagate query-aligned signals, producing discriminative exemplars. This enables precise selection of semantically aligned, task-relevant demonstrations, enhancing LVLMs for robust MDD. Experiments on four forgery types show that GASP-ICL surpasses strong baselines, delivering gains without LVLM fine-tuning.

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