Papers with Code 아카이브
Face Swapping
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/paarthneekhara/facesignsdemo
공식 코드FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes
https://github.com/parul-gupta/multifakeverse
공식 코드https://github.com/ph-w2000/s2pecnet
공식 코드Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms
https://github.com/piotrkawa/attack-agnostic-dataset
공식 코드Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection
https://github.com/piotrkawa/audio-deepfake-adversarial-attacks
공식 코드Defense Against Adversarial Attacks on Audio DeepFake Detection
https://github.com/piotrkawa/deepfake-whisper-features
공식 코드https://github.com/piotrkawa/specrnet
공식 코드SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection
https://github.com/polimi-ispl/jpeg-ai-antifor
공식 코드https://github.com/pratikpv/mri_gan_deepfake
공식 코드MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual Image Assessment
https://github.com/pterhoer/DeepFakeAnnotations
공식 코드Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
https://github.com/purdue-m2/ai-face-fairnessbench
공식 코드AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark
https://github.com/purdue-m2/auc_fairness_with_noisy_groups
공식 코드Preserving AUC Fairness in Learning with Noisy Protected Groups
https://github.com/purdue-m2/fairness-generalization
공식 코드https://github.com/purdue-m2/sp_cup
공식 코드https://github.com/qingyuliu/exposing-the-deception
공식 코드Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
https://github.com/qiqitao77/awesome-comprehensive-deepfake-detection
공식 코드Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey
https://github.com/qiqitao77/comprehensive-advances-in-deepfake-detection-spanning-diverse-modalities
공식 코드Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey
https://github.com/quarxilon/generator_attribution
공식 코드Transferable Class-Modelling for Decentralized Source Attribution of GAN-Generated Images
https://github.com/quereste/implicit-deepfake
공식 코드Deepfake for the Good: Generating Avatars through Face-Swapping with Implicit Deepfake Generation
https://github.com/raining-dev/avt2-dwf
공식 코드AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies
https://github.com/rainy-xu/tall4deepfake
공식 코드https://github.com/rama0126/pwtf-dvd
공식 코드Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection
https://github.com/razla/xai-based-detection-of-adversarial-attacks-on-deepfake-detectors
공식 코드XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
https://github.com/recluzegeek/AWARE-NET
공식 코드AWARE-NET: Adaptive Weighted Averaging for Robust Ensemble Network in Deepfake Detection
https://github.com/rgb91/temporal-deepfake-segmentation
공식 코드https://github.com/ristea/xmad-bench
공식 코드XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
https://github.com/rmushfiqur2/deepfake_op_rel
공식 코드Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations
https://github.com/rshaojimmy/deepfake-adapter
공식 코드https://github.com/rshaojimmy/multimodal-deepfake
공식 코드https://github.com/rshaojimmy/seqdeepfake
공식 코드https://github.com/rub-syssec/generatedmediasurvey
공식 코드A Representative Study on Human Detection of Artificially Generated Media Across Countries
https://github.com/rub-syssec/wavefake
공식 코드https://github.com/sahasubhajit/Speech-Spoofing-
공식 코드https://github.com/sanjaysaha1311/temporal-deepfake-segmentation
공식 코드https://github.com/sanoojan/reface
공식 코드Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models
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