BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912946643271680 |
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| author | Wen, Haiquan He, Yiwei Huang, Zhenglin Li, Tianxiao Yu, Zihan Huang, Xingru Qi, Lu Wu, Baoyuan Li, Xiangtai Cheng, Guangliang |
| author_facet | Wen, Haiquan He, Yiwei Huang, Zhenglin Li, Tianxiao Yu, Zihan Huang, Xingru Qi, Lu Wu, Baoyuan Li, Xiangtai Cheng, Guangliang |
| contents | As generative video models become increasingly realistic, detecting AI-generated videos requires systems that offer both accuracy and interpretability. However, applying Multimodal Large Language Models (MLLMs) to video forensics is currently limited by outdated datasets, simplistic evaluation protocols, and a reliance on black-box classification. To address these issues, we introduce a comprehensive dataset, benchmark, and baseline model for video forgery detection. First, we present \textbf{GenBuster-200K}, a fair dataset of over 200,000 high-quality videos sourced from state-of-the-art generators, featuring diverse real-world scenarios. Second, we propose \textbf{GenBuster-Bench}, a diagnostic benchmark spanning three progressive tracks (In-Domain, Out-of-Domain, and In-the-Wild) to evaluate models across \textit{domain shifts} and \textit{generational shifts}. It also introduces an MLLM-as-a-Judge protocol to assess the quality of the generated forensic explanations. Finally, we develop \textbf{BusterX}, an MLLM baseline with RL training. Instead of direct binary classification, BusterX formulates detection as a visual reasoning task, where the generated reasoning chain serves as detector itself. Experimental results demonstrate that BusterX outperforms several leading MLLMs (e.g., Qwen3.5, Claude-Sonnet-4.6) in both detection accuracy and rationale quality. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_12620 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation Wen, Haiquan He, Yiwei Huang, Zhenglin Li, Tianxiao Yu, Zihan Huang, Xingru Qi, Lu Wu, Baoyuan Li, Xiangtai Cheng, Guangliang Computer Vision and Pattern Recognition As generative video models become increasingly realistic, detecting AI-generated videos requires systems that offer both accuracy and interpretability. However, applying Multimodal Large Language Models (MLLMs) to video forensics is currently limited by outdated datasets, simplistic evaluation protocols, and a reliance on black-box classification. To address these issues, we introduce a comprehensive dataset, benchmark, and baseline model for video forgery detection. First, we present \textbf{GenBuster-200K}, a fair dataset of over 200,000 high-quality videos sourced from state-of-the-art generators, featuring diverse real-world scenarios. Second, we propose \textbf{GenBuster-Bench}, a diagnostic benchmark spanning three progressive tracks (In-Domain, Out-of-Domain, and In-the-Wild) to evaluate models across \textit{domain shifts} and \textit{generational shifts}. It also introduces an MLLM-as-a-Judge protocol to assess the quality of the generated forensic explanations. Finally, we develop \textbf{BusterX}, an MLLM baseline with RL training. Instead of direct binary classification, BusterX formulates detection as a visual reasoning task, where the generated reasoning chain serves as detector itself. Experimental results demonstrate that BusterX outperforms several leading MLLMs (e.g., Qwen3.5, Claude-Sonnet-4.6) in both detection accuracy and rationale quality. |
| title | BusterX: MLLM-Powered AI-Generated Video Forgery Detection and Explanation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.12620 |