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DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

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arxiv 2307.01426 v2 pith:Z7O77JUS submitted 2023-07-04 cs.CV

Zhiyuan Yan , Yong Zhang , Xinhang Yuan , Siwei Lyu , Baoyuan Wu This is my paper
classification cs.CV
keywords detectiondeepfakebenchmarkcomprehensivedatadeepfakebenchevaluationevaluations
verification ladder T0 review T1 audit T2 compute T3 formal
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A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection models. Additionally, there are noticeable differences in experimental settings, and evaluation strategies and metrics lack standardization. To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility. Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations. Moreover, we provide new insights based on extensive analysis of these evaluations from various perspectives (e.g., data augmentations, backbones). We hope that our efforts could facilitate future research and foster innovation in this increasingly critical domain. All codes, evaluations, and analyses of our benchmark are publicly available at https://github.com/SCLBD/DeepfakeBench.

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Cited by 10 Pith papers

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