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Face Swapping

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 )

Papers

Showing 776800 of 914 papers

TitleStatusHype
A Systematical Solution for Face De-identification0
Combining EfficientNet and Vision Transformers for Video Deepfake DetectionCode1
CoReD: Generalizing Fake Media Detection with Continual Representation using DistillationCode0
FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point DefectsCode1
Understanding the Security of Deepfake Detection0
ShapeEditer: a StyleGAN Encoder for Face Swapping0
Detection of Deepfake Videos Using Long Distance Attention0
Automated Deepfake Detection0
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face SwappingCode1
Wavelet-Packets for Deepfake Image Analysis and DetectionCode1
Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated ImagesCode1
SimSwap: An Efficient Framework For High Fidelity Face SwappingCode3
Imperceptible Adversarial Examples for Fake Image Detection0
DFGC 2021: A DeepFake Game CompetitionCode1
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning0
CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating DeepfakesCode1
Deepfake Detection by Human Crowds, Machines, and Machine-informed CrowdsCode0
TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised LearningCode0
What's wrong with this video? Comparing Explainers for Deepfake Detection0
One Shot Face Swapping on MegapixelsCode1
An Examination of Fairness of AI Models for Deepfake Detection0
Deep Insights of Deepfake Technology : A Review0
One Detector to Rule Them All: Towards a General Deepfake Attack Detection FrameworkCode1
DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning0
M2TR: Multi-modal Multi-scale Transformers for Deepfake DetectionCode1
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