Face Swapping
192 papers with code • 2 benchmarks • 9 datasets
Face swapping refers to the task of swapping faces between images or in an video, while maintaining the rest of the body and environment context.
( Image credit: Swapped Face Detection using Deep Learning and Subjective Assessment )
Benchmarks
These leaderboards are used to track progress in Face Swapping
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Libraries
Use these libraries to find Face Swapping models and implementationsDatasets
FaceForensics++
Celeb-DF
AFLW2000-3D
DFDC
WildDeepfake
DeeperForensics-1.0
VideoForensicsHQ
DFDM
VGGFace2 HQ
Most implemented papers
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Deepfake defense not only requires the research of detection but also requires the efforts of generation methods.
FaceForensics++: Learning to Detect Manipulated Facial Images
In particular, the benchmark is based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size.
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
We propose a novel attributes encoder for extracting multi-level target face attributes, and a new generator with carefully designed Adaptive Attentional Denormalization (AAD) layers to adaptively integrate the identity and the attributes for face synthesis.
MesoNet: a Compact Facial Video Forgery Detection Network
DariusAf/MesoNet
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This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face.
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information.
The DeepFake Detection Challenge (DFDC) Dataset
In addition to Deepfakes, a variety of GAN-based face swapping methods have also been published with accompanying code.
Face X-ray for More General Face Forgery Detection
For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms.
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
yuezunli/WIFS2018_In_Ictu_Oculi
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The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos.
Exposing DeepFake Videos By Detecting Face Warping Artifacts
yuezunli/CVPRW2019_Face_Artifacts
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Compared to previous methods which use a large amount of real and DeepFake generated images to train CNN classifier, our method does not need DeepFake generated images as negative training examples since we target the artifacts in affine face warping as the distinctive feature to distinguish real and fake images.
Combining EfficientNet and Vision Transformers for Video Deepfake Detection
davide-coccomini/Combining-EfficientNet-and-Vision-Transformersfor-Video-Deepfake-Detection
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Traditionally, Convolutional Neural Networks (CNNs) have been used to perform video deepfake detection, with the best results obtained using methods based on EfficientNet B7.
