Face Swapping

87 papers with code • 1 benchmarks • 8 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 )

Most implemented papers

DeepFaceLab: Integrated, flexible and extensible face-swapping framework

iperov/DeepFaceLab 12 May 2020

Deepfake defense not only requires the research of detection but also requires the efforts of generation methods.

FaceForensics++: Learning to Detect Manipulated Facial Images

ondyari/FaceForensics 25 Jan 2019

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

mindslab-ai/faceshifter 31 Dec 2019

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.

The DeepFake Detection Challenge (DFDC) Dataset

polimi-ispl/icpr2020dfdc 12 Jun 2020

In addition to Deepfakes, a variety of GAN-based face swapping methods have also been published with accompanying code.

MesoNet: a Compact Facial Video Forgery Detection Network

DariusAf/MesoNet 4 Sep 2018

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

danmohaha/celeb-deepfakeforensics CVPR 2020

AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information.

In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking

yuezunli/WIFS2018_In_Ictu_Oculi 7 Jun 2018

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 1 Nov 2018

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.

Face X-ray for More General Face Forgery Detection

neverUseThisName/Face-X-Ray CVPR 2020

For this reason, face X-ray provides an effective way for detecting forgery generated by most existing face manipulation algorithms.

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

deepfakes/faceswap 1 Jan 2020

The free access to large-scale public databases, together with the fast progress of deep learning techniques, in particular Generative Adversarial Networks, have led to the generation of very realistic fake content with its corresponding implications towards society in this era of fake news.