https://github.com/adauchendu/topformer
공식 코드TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles
Papers with Code 아카이브
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/adipiz99/RawNetLite
공식 코드https://github.com/adobe-research/videosham-dataset
공식 코드Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
https://github.com/adrianastan/tada
공식 코드TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
https://github.com/advboxes/AdvBox
공식 코드Advbox: a toolbox to generate adversarial examples that fool neural networks
https://github.com/ah651/deepfake_adv
공식 코드https://github.com/ai-forever/ghost
공식 코드A new face swap method for image and video domains: a technical report
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/ailon-island/facke
공식 코드https://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
https://github.com/ajev-research/agg-layers-deepfake-detection
공식 코드https://github.com/andrekassis/DiffBreak
공식 코드https://github.com/andrekassis/ai-watermark
공식 코드UnMarker: A Universal Attack on Defensive Image Watermarking
https://github.com/anmol2059/svdd2024
공식 코드https://github.com/anonrep/iba-for-video-sequences
공식 코드Extending Information Bottleneck Attribution to Video Sequences
https://github.com/anonymous-author-sub/seeable
공식 코드SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
https://github.com/anonymoususera/ava
공식 코드AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection
https://github.com/anubhav1997/youneednodataset
공식 코드A Dataless FaceSwap Detection Approach Using Synthetic Images
https://github.com/aravinda27/extswap
공식 코드ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality Face Swapping
https://github.com/arnesh2212/SocialDF
공식 코드https://github.com/asvspoof-challenge/2021
공식 코드ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
https://github.com/baidu/AdvBox
공식 코드Advbox: a toolbox to generate adversarial examples that fool neural networks
https://github.com/bit-ml/DeCLIP
공식 코드DeCLIP: Decoding CLIP representations for deepfake localization
https://github.com/bit-ml/avh-align
공식 코드Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning
https://github.com/bit-ml/dolos
공식 코드Weakly-supervised deepfake localization in diffusion-generated images
https://github.com/bok-bok/MSOC
공식 코드A Multi-Stream Fusion Approach with One-Class Learning for Audio-Visual Deepfake Detection
https://github.com/boutiquelee/deepfakeempiricalstudy
공식 코드How Generalizable are Deepfake Image Detectors? An Empirical Study
https://github.com/chandlerbing65nm/FakeImageDetection
공식 코드https://github.com/chelsea234/hifi_ifdl
공식 코드DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection
https://github.com/chelsea234/m2f2_det
공식 코드Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector
https://github.com/christophersims/fmri_adhd_classification
공식 코드Highly Accurate FMRI ADHD Classification using time distributed multi modal 3D CNNs
https://github.com/chuangchuangtan/c2p-clip-deepfakedetection
공식 코드C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection
https://github.com/chuangchuangtan/data-independent-operator
공식 코드https://github.com/chuangchuangtan/freqnet-deepfakedetection
공식 코드Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
https://github.com/chuangchuangtan/npr-deepfakedetection
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.