https://github.com/seymayucer/facialphenotypes
공식 코드Measuring Hidden Bias within Face Recognition via Racial Phenotypes
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sfzhang15/FaceBoxes
공식 코드https://github.com/sfzhang15/SFD
공식 코드https://github.com/sh1newu/advmark
공식 코드Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics
https://github.com/sh1newu/sepmark
공식 코드SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection
https://github.com/shahroztariq/CLRNet
공식 코드A Convolutional LSTM based Residual Network for Deepfake Video Detection
https://github.com/shaibagon/large_scale_cc
공식 코드https://github.com/shanface33/deepfake_model_attribution
공식 코드https://github.com/shanface33/gpt4mf_ub
공식 코드Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
https://github.com/shangeth/Speaker-Representation-for-Speaker-Profiling
공식 코드Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling
https://github.com/sharpstill/AU_R-CNN
공식 코드AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
https://github.com/shawnxyang/tip-im
공식 코드Towards Face Encryption by Generating Adversarial Identity Masks
https://github.com/shengfly/global-local-transformer
공식 코드https://github.com/sherrycattt/REGDet
공식 코드https://github.com/shighghyujie/newpatch-rl
공식 코드Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks
https://github.com/shiluo1990/SFA
공식 코드https://github.com/shiluo1990/bdc
공식 코드Bounding-box deep calibration for high performance face detection
https://github.com/shimmer-ghq/deepfidelity
공식 코드DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection
https://github.com/shrezaei/Target-Agnostic-Attack
공식 코드A Target-Agnostic Attack on Deep Models: Exploiting Security Vulnerabilities of Transfer Learning
https://github.com/shuchaoduan/qafe-net
공식 코드QAFE-Net: Quality Assessment of Facial Expressions with Landmark Heatmaps
https://github.com/shuvenduroy/activefer
공식 코드Active Learning with Contrastive Pre-training for Facial Expression Recognition
https://github.com/shuvenduroy/ssl_fer
공식 코드Analysis of Semi-Supervised Methods for Facial Expression Recognition
https://github.com/shuvenduroy/ssl_fer_ood
공식 코드https://github.com/siddiquelatif/URDU-Dataset
공식 코드Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
https://github.com/silence-tang/artnerf
공식 코드ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis
https://github.com/silvialaurapintea/reg-cls
공식 코드A step towards understanding why classification helps regression
https://github.com/simogroup/3dfair
공식 코드3D Facial Imperfection Regeneration: Deep learning approach and 3D printing prototypes
https://github.com/skjack/diffusionfake
공식 코드DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion
https://github.com/skrantidatta/lipinc
공식 코드https://github.com/skrantidatta/lipinc-v2
공식 코드Detecting Lip-Syncing Deepfakes: Vision Temporal Transformer for Analyzing Mouth Inconsistencies
https://github.com/skylar-sutherland/single-scan-3dmms
공식 코드https://github.com/slundberg/shap
공식 코드https://github.com/smile-ffg/he-man-concrete
공식 코드HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models
https://github.com/smuelpeng/sgps-noisefreedml
공식 코드https://github.com/socialabubi/iFakeFaceDB
공식 코드GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
https://github.com/sohailahmedkhan/CLIPping-the-Deception
공식 코드CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.