Do Deepfake Detectors Work in Reality?
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arXiv
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| Format: | Preprint |
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2025
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| author | Ren, Simiao Xu, Hengwei Ng, Tsang Zewde, Kidus Jiang, Shengkai Desai, Ramini Patil, Disha Cheng, Ning-Yau Zhou, Yining Muthukrishnan, Ragavi |
| author_facet | Ren, Simiao Xu, Hengwei Ng, Tsang Zewde, Kidus Jiang, Shengkai Desai, Ramini Patil, Disha Cheng, Ning-Yau Zhou, Yining Muthukrishnan, Ragavi |
| contents | Deepfakes, particularly those involving faceswap-based manipulations, have sparked significant societal concern due to their increasing realism and potential for misuse. Despite rapid advancements in generative models, detection methods have not kept pace, creating a critical gap in defense strategies. This disparity is further amplified by the disconnect between academic research and real-world applications, which often prioritize different objectives and evaluation criteria. In this study, we take a pivotal step toward bridging this gap by presenting a novel observation: the post-processing step of super-resolution, commonly employed in real-world scenarios, substantially undermines the effectiveness of existing deepfake detection methods. To substantiate this claim, we introduce and publish the first real-world faceswap dataset, collected from popular online faceswap platforms. We then qualitatively evaluate the performance of state-of-the-art deepfake detectors on real-world deepfakes, revealing that their accuracy approaches the level of random guessing. Furthermore, we quantitatively demonstrate the significant performance degradation caused by common post-processing techniques. By addressing this overlooked challenge, our study underscores a critical avenue for enhancing the robustness and practical applicability of deepfake detection methods in real-world settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Do Deepfake Detectors Work in Reality? Ren, Simiao Xu, Hengwei Ng, Tsang Zewde, Kidus Jiang, Shengkai Desai, Ramini Patil, Disha Cheng, Ning-Yau Zhou, Yining Muthukrishnan, Ragavi Computer Vision and Pattern Recognition Artificial Intelligence Deepfakes, particularly those involving faceswap-based manipulations, have sparked significant societal concern due to their increasing realism and potential for misuse. Despite rapid advancements in generative models, detection methods have not kept pace, creating a critical gap in defense strategies. This disparity is further amplified by the disconnect between academic research and real-world applications, which often prioritize different objectives and evaluation criteria. In this study, we take a pivotal step toward bridging this gap by presenting a novel observation: the post-processing step of super-resolution, commonly employed in real-world scenarios, substantially undermines the effectiveness of existing deepfake detection methods. To substantiate this claim, we introduce and publish the first real-world faceswap dataset, collected from popular online faceswap platforms. We then qualitatively evaluate the performance of state-of-the-art deepfake detectors on real-world deepfakes, revealing that their accuracy approaches the level of random guessing. Furthermore, we quantitatively demonstrate the significant performance degradation caused by common post-processing techniques. By addressing this overlooked challenge, our study underscores a critical avenue for enhancing the robustness and practical applicability of deepfake detection methods in real-world settings. |
| title | Do Deepfake Detectors Work in Reality? |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2502.10920 |