We lifted 2 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lalithbharadwajbaru/wavelet-clip | extension | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| choose_metric | Ran | lalithbharadwajbaru/wavelet-clip/training/gen_test.py code served (permissive licence) · get_code("6e51f7833599876f") |
| get_test_metrics | Ran | lalithbharadwajbaru/wavelet-clip/training/gen_test.py code served (permissive licence) · get_code("9662356c5c4cb76d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2307.01426")
get_code_for_paper("2307.01426")
have("2307.01426")
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