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Audio Deepfake Detection
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Nowadays, deepfake is now generically used by the media or people to refer to any audio or video in which important attributes have been either digitally altered or swapped, with the help of artificial intelligence (AI). Audio deepfake detection is a task that aims to distinguish genuine utterances from fake ones via machine learning techniques.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LetterLiGo/SafeEar
공식 코드SafeEar: Content Privacy-Preserving Audio Deepfake Detection
https://github.com/NetaGlazer/ADD-GP
공식 코드Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
https://github.com/Yuto-Matsunaga/Prompt_Tuning_for_Audio_Deepfake_Detection
공식 코드https://github.com/adipiz99/RawNetLite
공식 코드https://github.com/ai-s2-lab/m2s-add
공식 코드Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
https://github.com/awsaf49/sonics
공식 코드https://github.com/clad23/clad
공식 코드CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning
https://github.com/clovaai/aasist
공식 코드AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
https://github.com/cyjie429/rego
공식 코드Region-Based Optimization in Continual Learning for Audio Deepfake Detection
https://github.com/ductuantruong/tcm_add
공식 코드Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection
https://github.com/eurecom-asp/rawgat-st-antispoofing
공식 코드https://github.com/eurecom-asp/rawnet2-antispoofing
공식 코드https://github.com/jessegator/sonar
공식 코드Where are we in audio deepfake detection? A systematic analysis over generative and detection models
https://github.com/orchidchetiaphukan/multilingualptm_add_naacl24
공식 코드https://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/specrnet
공식 코드SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection
https://github.com/rub-syssec/wavefake
공식 코드https://github.com/sahasubhajit/Speech-Spoofing-
공식 코드https://github.com/swagshaw/xlsr-mamba
공식 코드XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection
https://github.com/talkingnow/HM-Conformer
공식 코드https://github.com/xieyuankun/Codecfake
공식 코드The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio
https://github.com/xieyuankun/all-type-add
공식 코드Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception
https://github.com/xieyuankun/alm-add
공식 코드Does Current Deepfake Audio Detection Model Effectively Detect ALM-based Deepfake Audio?
https://github.com/xieyuankun/fsd-dataset
공식 코드https://github.com/xieyuankun/st-codecfake
공식 코드Neural Codec Source Tracing: Toward Comprehensive Attribution in Open-Set Condition
https://github.com/yaselley/ssl_layerwise_deepfake
공식 코드Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection
https://github.com/Ashigarg123/ShiftySpeech
https://github.com/Manasi2001/Spoofed-Speech-Attribution
AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks
https://github.com/MindCode-4/code-5/tree/main/wavlm
https://github.com/QiShanZhang/SLSforASVspoof-2021-DF
https://github.com/audio-df-ucb/clonedvoicedetection
https://github.com/avishai111/One-Class-Learning-with-Adaptive-Centroid-Shift-for-Audio-Deepfake-Detection
One-Class Learning with Adaptive Centroid Shift for Audio Deepfake Detection
https://github.com/cecile-hi/regularized-adaptive-weight-modification
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
https://github.com/josebeo2016/BTS-Encoder-ASVspoof
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.