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DeepFaceLab: Integrated, flexible and extensible face-swapping framework

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arxiv 2005.05535 v5 pith:OIDKLLKJ submitted 2020-05-12 cs.CV cs.LGcs.MMeess.IV

Ivan Perov , Daiheng Gao , Nikolay Chervoniy , Kunlin Liu , Sugasa Marangonda , Chris Umé , Dpfks , Carl Shift Facenheim
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Luis RP Jian Jiang Sheng Zhang Pingyu Wu Bo Zhou Weiming Zhang
This is my paper · ORCID
classification cs.CVcs.LGcs.MMeess.IV
keywords deepfacelabface-swappingdeepfakemethodspipelineachievecurrentflexible
verification ladder T0 review T1 audit T2 compute T3 formal
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Deepfake defense not only requires the research of detection but also requires the efforts of generation methods. However, current deepfake methods suffer the effects of obscure workflow and poor performance. To solve this problem, we present DeepFaceLab, the current dominant deepfake framework for face-swapping. It provides the necessary tools as well as an easy-to-use way to conduct high-quality face-swapping. It also offers a flexible and loose coupling structure for people who need to strengthen their pipeline with other features without writing complicated boilerplate code. We detail the principles that drive the implementation of DeepFaceLab and introduce its pipeline, through which every aspect of the pipeline can be modified painlessly by users to achieve their customization purpose. It is noteworthy that DeepFaceLab could achieve cinema-quality results with high fidelity. We demonstrate the advantage of our system by comparing our approach with other face-swapping methods.For more information, please visit:https://github.com/iperov/DeepFaceLab/.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 87 citations worldwide. Full citation record

  1. SRAP: SVD-Refined Adversarial Perturbations for Imperceptible Face-Swap Defense

    cs.CV 2026-08 conditional novelty 6.0 of 10

    SRAP combines per-channel truncated SVD and an identity-importance mask to make PGD perturbations for face-swap defense more imperceptible while retaining competitive identity disruption.

  2. Adaptive Identity Anchoring: Closed-Loop Keyframe Placement for Synthetic Paired Supervision in Video Face Swapping

    cs.CV 2026-07 conditional novelty 6.0 of 10

    For video face swapping, adaptively adding swapped anchor frames at the moments of worst identity drift should make synthetic training pairs more faithful than the current first-and-last-frame-only scheme.

  3. Seeing Through Deepfakes: A Human-Inspired Framework for Multi-Face Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    HICOM is a multi-face deepfake detection framework whose four modules are each inspired by cues that humans reportedly use to spot fake faces, achieving state-of-the-art frame-level complete detection on existing benchmarks.

  4. Spotting tell-tale visual artifacts in face swapping videos: strengths and pitfalls of CNN detectors

    cs.CV 2025-06 conditional novelty 6.0 of 10

    CNN detectors for face-swap videos achieve near-perfect accuracy on the same dataset they are trained on, but cross-dataset accuracy drops dramatically, showing they learn dataset-specific cues rather than occlusion-b...

  5. Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)

    cs.AI 2026-05 conditional novelty 5.0 of 10

    The dominant real-world use of generative-image abuse is non-consensual intimate imagery, yet the AI/ML research field focuses almost exclusively on viewer deception.

  6. Evaluating Deepfake Detectors in the Wild

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Modern deepfake detectors, evaluated on a new 500,000 image in-the-wild style benchmark built with SimSwap and Inswapper, mostly fail to generalize and degrade under simple image manipulations.

  7. Visual Language Models as Zero-Shot Deepfake Detectors

    cs.CV 2025-07 conditional novelty 4.0 of 10

    Zero-shot VLMs scored by normalized yes/no token probabilities beat most trained deepfake detectors on a new SimSwap dataset, and a lightly fine-tuned InstructBLIP is near-perfect on DFDC-P.

  8. SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection

    cs.CV 2025-06 conditional novelty 4.0 of 10

    A spatial-frequency feature fusion network with attention achieves improved accuracy on remote sensing image forgery detection and introduces a stable-diffusion-based benchmark.

  9. Face Deepfakes -- A Comprehensive Review

    cs.CV 2025-02 conditional novelty 3.0 of 10

    A review of face deepfake generation and detection finds that off-the-shelf deepfake tools such as Wav2Lip and SimSwap achieve high attack success rates against lightweight face recognition models.

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