Face Swapping
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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/liu-tianchi/nes2net
공식 코드Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing
https://github.com/ljj898/cmdfd-dataset-and-deepfake-detection
공식 코드Explicit Correlation Learning for Generalizable Cross-Modal Deepfake Detection
https://github.com/ljuvela/collaborative-watermarking-with-codecs
공식 코드Audio Codec Augmentation for Robust Collaborative Watermarking of Speech Synthesis
https://github.com/lovesiamesecat/favit
공식 코드Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer
https://github.com/lsy0882/FakeMix
공식 코드WWW: Where, Which and Whatever Enhancing Interpretability in Multimodal Deepfake Detection
https://github.com/lxq1000/dfa-instruct
공식 코드https://github.com/magnet300/camme
공식 코드CAMME: Adaptive Deepfake Image Detection with Multi-Modal Cross-Attention
https://github.com/manyilee/plada
공식 코드https://github.com/mapooon/blendface
공식 코드https://github.com/mattgroh/cognitive-science-detecting-deepfakes
공식 코드Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds
https://github.com/megvii-research/caddm
공식 코드Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization
https://github.com/megvii-research/fst-matching
공식 코드https://github.com/mehrdadsaberi/watermark_robustness
공식 코드Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
https://github.com/mesnico/idclip
공식 코드Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a Baseline
https://github.com/miaoct/msccnet
공식 코드Multi-spectral Class Center Network for Face Manipulation Detection and Localization
https://github.com/ming053l/grace
공식 코드https://github.com/miraflow/DistilDIRE
공식 코드DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection
https://github.com/mit-ll/lorax_cil
공식 코드LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
https://github.com/mittalgovind/GOTCHA-Deepfakes
공식 코드GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response
https://github.com/monu-khicher-1/multi-stage-learning
공식 코드https://github.com/mouse1998mouse/S-xception
공식 코드Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection
https://github.com/nctu-eva-lab/antifakeprompt
공식 코드AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
https://github.com/neuralchen/SimSwap
공식 코드SimSwap: An Efficient Framework For High Fidelity Face Swapping
https://github.com/nice-x/dfgc-2022
공식 코드https://github.com/nicksunday/deepfake-music-detector
공식 코드https://github.com/nii-yamagishilab/antideepfake
공식 코드https://github.com/nii-yamagishilab/ssl_vits_df
공식 코드Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis
https://github.com/nilslukas/gan-watermark
공식 코드PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
https://github.com/ningyu1991/ArtificialGANFingerprints
공식 코드Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
https://github.com/nowindbutrain/blockshufflelearning
공식 코드https://github.com/nuriachandra/deepfake-eval-2024
공식 코드Deepfake-Eval-2024: A Multi-Modal In-the-Wild Benchmark of Deepfakes Circulated in 2024
https://github.com/nyu-dice-lab/circumventing-concept-erasure
공식 코드Circumventing Concept Erasure Methods For Text-to-Image Generative Models
https://github.com/oidelima/Deepfake-Detection
공식 코드Deepfake Detection using Spatiotemporal Convolutional Networks
https://github.com/ondyari/FaceForensics
공식 코드FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/opontorno/dcts_analysis_deepfakes
공식 코드On the Exploitation of DCT-Traces in the Generative-AI Domain
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.