https://github.com/BiDAlab/DeepFakesON-Phys
DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation
Papers with Code 아카이브
DeepFake Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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DeepFake Detection is the task of detecting fake videos or images that have been generated using deep learning techniques. Deepfakes are created by using machine learning algorithms to manipulate or replace parts of an original video or image, such as the face of a person. The goal of deepfake detection is to identify such manipulations and distinguish them from real videos or images.
Description source: DeepFakes: a New Threat to Face Recognition? Assessment and Detection
Image source: DeepFakes: a New Threat to Face Recognition? Assessment and Detection
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CatoGit/Comparing-the-Performance-of-Deepfake-Detection-Methods-on-Benchmark-Datasets
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
https://github.com/Daisy-Zhang/Face-X-ray
https://github.com/DataScienceNigeria/Fake-Detection-dataset-for-deepfake-from-Google-and-Jigsaw
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/FaceOnLive/DeepFake-Detection-SDK-Linux
https://github.com/HongguLiu/MesoNet.Pytorch
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/convnext
https://github.com/MalayAgarwal-Lee/MesoNet-DeepFakeDetection
https://github.com/Manasi2001/Spoofed-Speech-Attribution
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
https://github.com/ManyiLee/Open-world-Deepfake-Detection-Network
ODDN: Addressing Unpaired Data Challenges in Open-World Deepfake Detection on Online Social Networks
https://github.com/MindCode-4/code-5/tree/main/wavlm
https://github.com/MindSpore-paper-code-3/code2/tree/main/convnext
https://github.com/QiShanZhang/SLSforASVspoof-2021-DF
https://github.com/Raj-08/Deepfake-Detection-Mesonet
https://github.com/Ramneantu/deepfake-detection
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
Unmasking Deepfake Faces from Videos Using An Explainable Cost-Sensitive Deep Learning Approach
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
https://github.com/SuyashSonawane/fakedetector
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/YvanG/VQGAN-CLIP
https://github.com/aiiu-lab/dfd-fcg
https://github.com/aimagelab/elsa_coco-fake
Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
https://github.com/alibaba/EasyNLP/tree/master/examples/text2image_generation
https://github.com/ammarahhashmi/Multimodal-Forgery-Detection-Using-Ensemble-Learning
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/bigaandsmallq/d3
D$^3$: Scaling Up Deepfake Detection by Learning from Discrepancy
https://github.com/cc-hpc-itwm/UpConv
https://github.com/cecile-hi/regularized-adaptive-weight-modification
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
https://github.com/deepfakes/faceswap
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
https://github.com/dome272/VQGAN
https://github.com/dome272/vqgan-pytorch
https://github.com/flynn-chen/faceforensics_benchmark
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/gewanying/shap-anti-spoofing
https://github.com/ghua-ac/end-to-end-synthetic-speech-detection
https://github.com/horizontel/aigibench
Generalizable Deepfake Detection via Effective Local-Global Feature Extraction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.