AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations
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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_ | 1866912505035489280 |
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| author | Cai, Zhixi Kuckreja, Kartik Ghosh, Shreya Chuchra, Akanksha Khan, Muhammad Haris Tariq, Usman Gedeon, Tom Dhall, Abhinav |
| author_facet | Cai, Zhixi Kuckreja, Kartik Ghosh, Shreya Chuchra, Akanksha Khan, Muhammad Haris Tariq, Usman Gedeon, Tom Dhall, Abhinav |
| contents | The rapid surge of text-to-speech and face-voice reenactment models makes video fabrication easier and highly realistic. To encounter this problem, we require datasets that rich in type of generation methods and perturbation strategy which is usually common for online videos. To this end, we propose AV-Deepfake1M++, an extension of the AV-Deepfake1M having 2 million video clips with diversified manipulation strategy and audio-visual perturbation. This paper includes the description of data generation strategies along with benchmarking of AV-Deepfake1M++ using state-of-the-art methods. We believe that this dataset will play a pivotal role in facilitating research in Deepfake domain. Based on this dataset, we host the 2025 1M-Deepfakes Detection Challenge. The challenge details, dataset and evaluation scripts are available online under a research-only license at https://deepfakes1m.github.io/2025. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20579 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations Cai, Zhixi Kuckreja, Kartik Ghosh, Shreya Chuchra, Akanksha Khan, Muhammad Haris Tariq, Usman Gedeon, Tom Dhall, Abhinav Computer Vision and Pattern Recognition The rapid surge of text-to-speech and face-voice reenactment models makes video fabrication easier and highly realistic. To encounter this problem, we require datasets that rich in type of generation methods and perturbation strategy which is usually common for online videos. To this end, we propose AV-Deepfake1M++, an extension of the AV-Deepfake1M having 2 million video clips with diversified manipulation strategy and audio-visual perturbation. This paper includes the description of data generation strategies along with benchmarking of AV-Deepfake1M++ using state-of-the-art methods. We believe that this dataset will play a pivotal role in facilitating research in Deepfake domain. Based on this dataset, we host the 2025 1M-Deepfakes Detection Challenge. The challenge details, dataset and evaluation scripts are available online under a research-only license at https://deepfakes1m.github.io/2025. |
| title | AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.20579 |