Do Deepfake Detectors Work in Reality?

• URL: http://arxiv.org/abs/2502.10920v1
• Date: Sat, 15 Feb 2025 22:38:40 GMT
• Title: Do Deepfake Detectors Work in Reality?
• Authors: Simiao Ren, Hengwei Xu, Tsang Ng, Kidus Zewde, Shengkai Jiang, Ramini Desai, Disha Patil, Ning-Yau Cheng, Yining Zhou, Ragavi Muthukrishnan,
• Abstract summary: Deepfakes, particularly those involving faceswap-based manipulations, have sparked significant societal concern.<n>Despite rapid advancements in generative models, detection methods have not kept pace, creating a critical gap in defense strategies.<n>This study presents 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.
• Score: 3.230104201410257
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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