DeepfakeBench-MM: A Comprehensive Benchmark for Multimodal Deepfake Detection
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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_ | 1866909870134919168 |
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| author | Zhao, Kangran Chen, Yupeng Zhang, Xiaoyu Chen, Yize Guan, Weinan Chen, Baicheng Sun, Chengzhe Datta, Soumyya Kanti Liu, Qingshan Lyu, Siwei Wu, Baoyuan |
| author_facet | Zhao, Kangran Chen, Yupeng Zhang, Xiaoyu Chen, Yize Guan, Weinan Chen, Baicheng Sun, Chengzhe Datta, Soumyya Kanti Liu, Qingshan Lyu, Siwei Wu, Baoyuan |
| contents | The misuse of advanced generative AI models has resulted in the widespread proliferation of falsified data, particularly forged human-centric audiovisual content, which poses substantial societal risks (e.g., financial fraud and social instability). In response to this growing threat, several works have preliminarily explored countermeasures. However, the lack of sufficient and diverse training data, along with the absence of a standardized benchmark, hinder deeper exploration. To address this challenge, we first build Mega-MMDF, a large-scale, diverse, and high-quality dataset for multimodal deepfake detection. Specifically, we employ 21 forgery pipelines through the combination of 10 audio forgery methods, 12 visual forgery methods, and 6 audio-driven face reenactment methods. Mega-MMDF currently contains 0.1 million real samples and 1.1 million forged samples, making it one of the largest and most diverse multimodal deepfake datasets, with plans for continuous expansion. Building on it, we present DeepfakeBench-MM, the first unified benchmark for multimodal deepfake detection. It establishes standardized protocols across the entire detection pipeline and serves as a versatile platform for evaluating existing methods as well as exploring novel approaches. DeepfakeBench-MM currently supports 5 datasets and 11 multimodal deepfake detectors. Furthermore, our comprehensive evaluations and in-depth analyses uncover several key findings from multiple perspectives (e.g., augmentation, stacked forgery). We believe that DeepfakeBench-MM, together with our large-scale Mega-MMDF, will serve as foundational infrastructures for advancing multimodal deepfake detection. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_22622 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepfakeBench-MM: A Comprehensive Benchmark for Multimodal Deepfake Detection Zhao, Kangran Chen, Yupeng Zhang, Xiaoyu Chen, Yize Guan, Weinan Chen, Baicheng Sun, Chengzhe Datta, Soumyya Kanti Liu, Qingshan Lyu, Siwei Wu, Baoyuan Cryptography and Security Computer Vision and Pattern Recognition Multimedia The misuse of advanced generative AI models has resulted in the widespread proliferation of falsified data, particularly forged human-centric audiovisual content, which poses substantial societal risks (e.g., financial fraud and social instability). In response to this growing threat, several works have preliminarily explored countermeasures. However, the lack of sufficient and diverse training data, along with the absence of a standardized benchmark, hinder deeper exploration. To address this challenge, we first build Mega-MMDF, a large-scale, diverse, and high-quality dataset for multimodal deepfake detection. Specifically, we employ 21 forgery pipelines through the combination of 10 audio forgery methods, 12 visual forgery methods, and 6 audio-driven face reenactment methods. Mega-MMDF currently contains 0.1 million real samples and 1.1 million forged samples, making it one of the largest and most diverse multimodal deepfake datasets, with plans for continuous expansion. Building on it, we present DeepfakeBench-MM, the first unified benchmark for multimodal deepfake detection. It establishes standardized protocols across the entire detection pipeline and serves as a versatile platform for evaluating existing methods as well as exploring novel approaches. DeepfakeBench-MM currently supports 5 datasets and 11 multimodal deepfake detectors. Furthermore, our comprehensive evaluations and in-depth analyses uncover several key findings from multiple perspectives (e.g., augmentation, stacked forgery). We believe that DeepfakeBench-MM, together with our large-scale Mega-MMDF, will serve as foundational infrastructures for advancing multimodal deepfake detection. |
| title | DeepfakeBench-MM: A Comprehensive Benchmark for Multimodal Deepfake Detection |
| topic | Cryptography and Security Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2510.22622 |