https://github.com/phd-research-ai/tca2
공식 코드Transferable Adversarial Face Attack with Text Controlled Attribute
Papers with Code 아카이브
Facial Recognition and Modelling
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Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/po0ya/face-magnet
공식 코드https://github.com/polimi-ispl/jpeg-ai-antifor
공식 코드https://github.com/polo5/ldeq_rwr
공식 코드Recurrence without Recurrence: Stable Video Landmark Detection with Deep Equilibrium Models
https://github.com/ppolon/dcp
공식 코드Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition
https://github.com/pratikpv/mri_gan_deepfake
공식 코드MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual Image Assessment
https://github.com/prclibo/ice
공식 코드https://github.com/prevso1088/open-set-biometrics
공식 코드https://github.com/prijatelj/bayesian_eval_ground_truth-free
공식 코드A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks
https://github.com/protossw512/AdaptiveWingLoss
공식 코드Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
https://github.com/pterhoer/DeepFakeAnnotations
공식 코드Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
https://github.com/pterhoer/ExplainableFaceImageQuality
공식 코드Pixel-Level Face Image Quality Assessment for Explainable Face Recognition
https://github.com/pterhoer/FairScoreNormalization
공식 코드Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
https://github.com/pterhoer/MAAD-Face
공식 코드MAAD-Face: A Massively Annotated Attribute Dataset for Face Images
https://github.com/pterhoer/PrivacyPreservingFaceRecognition
공식 코드Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition
https://github.com/pterhoer/QMagFace
공식 코드QMagFace: Simple and Accurate Quality-Aware Face Recognition
https://github.com/pterhoer/optimalmatchingconfidence
공식 코드https://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/purpleleaves007/ferdenoise
공식 코드ReSup: Reliable Label Noise Suppression for Facial Expression Recognition
https://github.com/pvskand/DisguiseNet
공식 코드DisguiseNet : A Contrastive Approach for Disguised Face Verification in the Wild
https://github.com/qianyuzqy/iadg
공식 코드https://github.com/qingyuliu/exposing-the-deception
공식 코드Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
https://github.com/qinxiaoyi/cross-age_speaker_verification
공식 코드Cross-Age Speaker Verification: Learning Age-Invariant Speaker Embeddings
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/qizhangli/nobox-attacks
공식 코드https://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/radekd91/emoca
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.