Face Swapping
206 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
Undercover Deepfakes: Detecting Fake Segments in Videos
rgb91/temporal-deepfake-segmentation
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This paradigm has been under-explored by the current deepfake detection methods in the academic literature.
MAGE: Machine-generated Text Detection in the Wild
yafuly/mage
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In practical scenarios, however, the detector faces texts from various domains or LLMs without knowing their sources.
BlendFace: Re-designing Identity Encoders for Face-Swapping
mapooon/blendface
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ICCV 2023
The great advancements of generative adversarial networks and face recognition models in computer vision have made it possible to swap identities on images from single sources.
E4S: Fine-grained Face Swapping via Editing With Regional GAN Inversion
e4s2024/e4s2024
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Based on this disentanglement, face swapping can be simplified as style and mask swapping.
DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection
Considering the complexity of the quality distribution of both real and fake faces, we propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces with varying image quality by mining the perceptual forgery fidelity of face images.
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
chuangchuangtan/npr-deepfakedetection
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Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse.
Learning to Detect Fake Face Images in the Wild
jesse1029/Fake-Face-Images-Detection-Tensorflow
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Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns.
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
CatoGit/Comparing-the-Performance-of-Deepfake-Detection-Methods-on-Benchmark-Datasets
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The best performing method, which is based on visual quality metrics and is often used in presentation attack detection domain, resulted in 8. 97% equal error rate on high quality Deepfakes.
Recurrent Convolutional Strategies for Face Manipulation Detection in Videos
The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods.
Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos
nii-yamagishilab/ClassNSeg
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The output of one branch of the decoder is used for segmenting the manipulated regions while that of the other branch is used for reconstructing the input, which helps improve overall performance.
