SCLBD/DeepfakeBench

Health v2 · maintenance only

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1093 stars · 183 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DeepfakeBench is a comprehensive benchmark framework for deepfake detection, providing a unified platform for data management, implementation of state-of-the-art detection methods, and standardized evaluation protocols. It was published at NeurIPS 2023 (Datasets & Benchmarks) and includes pre-trained weights and integration with datasets like Celeb-DF, FF++, and DF40.

Use cases

• benchmark deepfake detection methods under a unified evaluation protocol
• train and evaluate face forgery detection models on standard datasets
• compare state-of-the-art deepfake detectors reproducibly
• test detection models on new deepfake datasets like DF40
• build new deepfake detection methods on an integrated framework
• evaluate detectors on both face deepfakes and synthetic images

When to choose

• you need standardized, reproducible comparison of deepfake detection methods
• you want pre-trained weights and unified data pipelines for face forgery detection research
• you are developing a new detector and want a common training/evaluation framework

When to avoid

• you need a production-ready deepfake detection service rather than a research benchmark
• your use case requires a permissive commercial license (project is CC BY-NC 4.0)
• you work outside Python/PyTorch environments

Facets

framework · maturity active

machine-learning benchmarking image-processing computer-vision testing deep-learning computer-vision artificial-intelligence security python deepfake-detection benchmark pytorch face-forgery media-forensics reproducibility research linux gpu

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markdown · JSON · MCP: product_card(name="SCLBD/DeepfakeBench")