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3D Shape Reconstruction from Videos
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AntonFirc/diffusion-deepfake-speech-dataset
공식 코드https://github.com/Ashigarg123/ShiftySpeech
공식 코드ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts
https://github.com/ChenqiKONG/OSDFD
공식 코드Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture
https://github.com/Clench/TAR_resAE
공식 코드TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning
https://github.com/CompVis/taming-transformers
공식 코드https://github.com/ControlNet/LAV-DF
공식 코드https://github.com/ControlNet/MARLIN
공식 코드MARLIN: Masked Autoencoder for facial video Representation LearnINg
https://github.com/Coral79/CDDB
공식 코드A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials
https://github.com/DariusAf/MesoNet
공식 코드https://github.com/HuSTeP-Human-Speech-Text-Processing-Lab/XLSR-GRKAN-Conformer
공식 코드https://github.com/IDT-ITI/Adv-XAI-Deepfakes
공식 코드https://github.com/Jessegator/Audio_robustness_evaluation
공식 코드https://github.com/KamruzzamanAsif/BanglaFake
공식 코드BanglaFake: Constructing and Evaluating a Specialized Bengali Deepfake Audio Dataset
https://github.com/Leminhbinh0209/AAAI22-ADD
공식 코드https://github.com/LetterLiGo/SafeEar
공식 코드SafeEar: Content Privacy-Preserving Audio Deepfake Detection
https://github.com/MZMMSEC/SJEDD
공식 코드Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach
https://github.com/NetaGlazer/ADD-GP
공식 코드Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
https://github.com/NickyFot/HitchhikersGuide
공식 코드A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning
https://github.com/OpenRL-Lab/DeepFakeFace
공식 코드Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models
https://github.com/OpenTAI/wild-deepfake
공식 코드WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
https://github.com/PalomaCantero/Deepfake-Detection-and-the-Impact-of-Limited-Computing-Capabilities
공식 코드Deepfake Detection and the Impact of Limited Computing Capabilities
https://github.com/TencentYoutuResearch/OpenWorld-DeepFakeAttribution
공식 코드Contrastive Pseudo Learning for Open-World DeepFake Attribution
https://github.com/YZY-stack/DF40
공식 코드https://github.com/Yuto-Matsunaga/Prompt_Tuning_for_Audio_Deepfake_Detection
공식 코드https://github.com/aakash4305/gbdf
공식 코드GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection
https://github.com/aaroncomo/lipfd
공식 코드https://github.com/abhinavdhall/deepfake
공식 코드Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
https://github.com/adipiz99/RawNetLite
공식 코드https://github.com/adrianastan/tada
공식 코드TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
https://github.com/ah651/deepfake_adv
공식 코드https://github.com/ahaliassos/RealForensics
공식 코드Leveraging Real Talking Faces via Self-Supervision for Robust Forgery Detection
https://github.com/ahaliassos/lipforensics
공식 코드Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection
https://github.com/ai-s2-lab/m2s-add
공식 코드Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
https://github.com/aimagelab/COCOFake
공식 코드Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
https://github.com/aimagelab/code
공식 코드Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.