REVIEW 7 cited by
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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/.
Forward citations
Cited by 7 Pith papers
-
How to Stop Playing Whack-a-Mole: Mapping the Ecosystem of Technologies Facilitating AI-Generated Non-Consensual Intimate Images
The paper introduces the first comprehensive taxonomy and visualization of 11 categories of technologies facilitating AI-generated non-consensual intimate images, derived from synthesis of primary sources and demonstr...
-
SRAP: SVD-Refined Adversarial Perturbations for Imperceptible Face-Swap Defense
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.
-
Adaptive Identity Anchoring: Closed-Loop Keyframe Placement for Synthetic Paired Supervision in Video Face Swapping
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.
-
Seeing Through Deepfakes: A Human-Inspired Framework for Multi-Face Detection
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.
-
Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)
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.
-
Evaluating Deepfake Detectors in the Wild
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.
-
Visual Language Models as Zero-Shot Deepfake Detectors
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.
Discussion (0). Sign in to comment.