{"ID":2864946,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21774","arxiv_id":"2509.21774","title":"Training-Free Multimodal Deepfake Detection via Graph Reasoning","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.21774","url_pdf":"https://arxiv.org/pdf/2509.21774v1","authors":"[\"Yuxin Liu\",\"Fei Wang\",\"Kun Li\",\"Yiqi Nie\",\"Junjie Chen\",\"Yanyan Wei\",\"Zhangling Duan\",\"Zhaohong Jia\"]","published":"2025-09-26T02:22:12Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.CY\"]","methods":"[\"Language Model\"]","has_code":false}