Face Swapping
203 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
FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset
We generate this dataset using the most popular deepfake generation methods.
Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations
slundberg/shap
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Substantial progress in spoofing and deepfake detection has been made in recent years.
WaveFake: A Data Set to Facilitate Audio Deepfake Detection
Deep generative modeling has the potential to cause significant harm to society.
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher's and student's tensors under different views to transfer the teacher tensor's distribution to the student more efficiently.
Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
davide-coccomini/Cross-Forgery-Analysis-of-Vision-Transformers-and-CNNs-for-Deepfake-Image-Detection
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Deepfake Generation Techniques are evolving at a rapid pace, making it possible to create realistic manipulated images and videos and endangering the serenity of modern society.
Masked Relation Learning for DeepFake Detection
zimyang/maskrelation
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2023 2023
A relation learning module masks partial correlations between regions to reduce redundancy and then propagates the relational information across regions to capture the irregularity from a global view of the graph.
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Advances in Large Language Models (e. g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts.
PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
We propose an adaptive attack that can successfully remove any watermarking with access to only 200 non-watermarked images.
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.
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.
