Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912638657626112 |
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| author | Shi, Junyu Li, Minghui Zuo, Junguo Yu, Zhifei Lin, Yipeng Hu, Shengshan Zhou, Ziqi Zhang, Yechao Wan, Wei Xu, Yinzhe Zhang, Leo Yu |
| author_facet | Shi, Junyu Li, Minghui Zuo, Junguo Yu, Zhifei Lin, Yipeng Hu, Shengshan Zhou, Ziqi Zhang, Yechao Wan, Wei Xu, Yinzhe Zhang, Leo Yu |
| contents | Deepfakes, leveraging advanced AIGC (Artificial Intelligence-Generated Content) techniques, create hyper-realistic synthetic images and videos of human faces, posing a significant threat to the authenticity of social media. While this real-world threat is increasingly prevalent, existing academic evaluations and benchmarks for detecting deepfake forgery often fall short to achieve effective application for their lack of specificity, limited deepfake diversity, restricted manipulation techniques.To address these limitations, we introduce RedFace (Real-world-oriented Deepfake Face), a specialized facial deepfake dataset, comprising over 60,000 forged images and 1,000 manipulated videos derived from authentic facial features, to bridge the gap between academic evaluations and real-world necessity. Unlike prior benchmarks, which typically rely on academic methods to generate deepfakes, RedFace utilizes 9 commercial online platforms to integrate the latest deepfake technologies found "in the wild", effectively simulating real-world black-box scenarios.Moreover, RedFace's deepfakes are synthesized using bespoke algorithms, allowing it to capture diverse and evolving methods used by real-world deepfake creators. Extensive experimental results on RedFace (including cross-domain, intra-domain, and real-world social network dissemination simulations) verify the limited practicality of existing deepfake detection schemes against real-world applications. We further perform a detailed analysis of the RedFace dataset, elucidating the reason of its impact on detection performance compared to conventional datasets. Our dataset is available at: https://github.com/kikyou-220/RedFace. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08067 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces Shi, Junyu Li, Minghui Zuo, Junguo Yu, Zhifei Lin, Yipeng Hu, Shengshan Zhou, Ziqi Zhang, Yechao Wan, Wei Xu, Yinzhe Zhang, Leo Yu Computer Vision and Pattern Recognition Deepfakes, leveraging advanced AIGC (Artificial Intelligence-Generated Content) techniques, create hyper-realistic synthetic images and videos of human faces, posing a significant threat to the authenticity of social media. While this real-world threat is increasingly prevalent, existing academic evaluations and benchmarks for detecting deepfake forgery often fall short to achieve effective application for their lack of specificity, limited deepfake diversity, restricted manipulation techniques.To address these limitations, we introduce RedFace (Real-world-oriented Deepfake Face), a specialized facial deepfake dataset, comprising over 60,000 forged images and 1,000 manipulated videos derived from authentic facial features, to bridge the gap between academic evaluations and real-world necessity. Unlike prior benchmarks, which typically rely on academic methods to generate deepfakes, RedFace utilizes 9 commercial online platforms to integrate the latest deepfake technologies found "in the wild", effectively simulating real-world black-box scenarios.Moreover, RedFace's deepfakes are synthesized using bespoke algorithms, allowing it to capture diverse and evolving methods used by real-world deepfake creators. Extensive experimental results on RedFace (including cross-domain, intra-domain, and real-world social network dissemination simulations) verify the limited practicality of existing deepfake detection schemes against real-world applications. We further perform a detailed analysis of the RedFace dataset, elucidating the reason of its impact on detection performance compared to conventional datasets. Our dataset is available at: https://github.com/kikyou-220/RedFace. |
| title | Towards Real-World Deepfake Detection: A Diverse In-the-wild Dataset of Forgery Faces |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.08067 |