多模态（推理）LLM 能否胜任 Deepfake 检测？12 个模型基准评测

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Abstract:Deepfake detection remains a critical challenge in the era of advanced generative models, particularly as synthetic media becomes more sophisticated. In this study, we explore the potential of state of the art multi-modal (reasoning) large language models (LLMs) for deepfake image detection such as (OpenAI O1/4o, Gemini thinking Flash 2, Deepseek Janus, Grok 3, llama 3.2, Qwen 2/2.5 VL, Mistral Pixtral, Claude 3.5/3.7 sonnet) . We benchmark 12 latest multi-modal LLMs against traditional deepfake detection methods across multiple datasets, including recently published real-world deepfake imagery. To enhance performance, we employ prompt tuning and conduct an in-depth analysis of the models' reasoning pathways to identify key contributing factors in their decision-making process. Our findings indicate that best multi-modal LLMs achieve competitive performance with promising generalization ability with zero shot, even surpass traditional deepfake detection pipelines in out-of-distribution datasets while the rest of the LLM families performs extremely disappointing with some worse than random guess. Furthermore, we found newer model version and reasoning capabilities does not contribute to performance in such niche tasks of deepfake detection while model size do help in some cases. This study highlights the potential of integrating multi-modal reasoning in future deepfake detection frameworks and provides insights into model interpretability for robustness in real-world scenarios.

Subjects:	Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2503.20084 [cs.CV]
	(or arXiv:2503.20084v3 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2503.20084 arXiv-issued DOI via DataCite

Submission history

From: Simiao Ren [view email]
[v1] Tue, 25 Mar 2025 21:47:29 UTC (7,022 KB)
[v2] Sat, 29 Mar 2025 19:19:14 UTC (7,653 KB)
[v3] Sat, 24 Oct 2026 21:33:33 UTC (7,654 KB)