https://github.com/ali-vilab/FlashFace
FlashFace: Human Image Personalization with High-fidelity Identity Preservation
Papers with Code 아카이브
Face Swapping
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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.
<span style="color:grey; opacity: 0.6">( Image credit: Swapped Face Detection using Deep Learning and Subjective Assessment )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alsgkals2/CoReD_Released
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
https://github.com/alsgkals2/cored
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
https://github.com/audio-df-ucb/clonedvoicedetection
https://github.com/avishai111/One-Class-Learning-with-Adaptive-Centroid-Shift-for-Audio-Deepfake-Detection
One-Class Learning with Adaptive Centroid Shift for Audio Deepfake Detection
https://github.com/beibuwandeluori/DFGC_Detection
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
https://github.com/bellsrik86/DeepFaceLab
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/beskal80/KHAIREPO
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/big-bombom/deepfake
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/bigaandsmallq/d3
D$^3$: Scaling Up Deepfake Detection by Learning from Discrepancy
https://github.com/cecile-hi/regularized-adaptive-weight-modification
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
https://github.com/code-implementation1/Code9/tree/main/Arcface
https://github.com/danmohaha/CVPRW2019_Face_Artifacts
Exposing DeepFake Videos By Detecting Face Warping Artifacts
https://github.com/danmohaha/WIFS2018_In_Ictu_Oculi
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
https://github.com/deepfakes/faceswap
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
https://github.com/denis19973/faceshifter_tornado
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/e4s2022/e4s
https://github.com/elhamod/phylonn
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
https://github.com/faddey-w/face-swap
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/flynn-chen/faceforensics_benchmark
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/hectorgie/DeepFaceLab
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/horizontel/aigibench
Generalizable Deepfake Detection via Effective Local-Global Feature Extraction
https://github.com/huashen218/multiturncleanup
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
https://github.com/huawenwei4/dfl
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/hyhchaos/FakePolisher
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
https://github.com/hzlsaber/sida
https://github.com/jackaduma/nude-detect
https://github.com/jhchang/DFDC
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/jtchen0528/PCL-I2G
https://github.com/kartik-3004/facexbench
https://github.com/kukico/learning
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/maragori/DeepfakeForensics-v1
https://github.com/mflames0327/DF-MOUTH
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/mindslab-ai/faceshifter
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/mindslab-ai/hififace
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
https://github.com/mrv8x/DeepFaceLab
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.