[!IMPORTANT]

A curated collection of papers and resources on Audio Deepfake Detection (ADD).

Please refer to our survey "Research progress on speech deepfake and its detection techniques" for the detailed contents.

Please let us know if you discover any mistakes or have suggestions by emailing us: xuyuxiong2022@email.szu.edu.cn

Table of contents

• Table of contents
• What's New
• Survey
• Top Repositories
• Audio Large Model
• Datasets
• Audio Preprocessing
  • Commonly Used Noise Datasets
  • Audio Enhancement Methods
• Feature Extraction
  • Handcrafted Feature-based Forgery Detection
  • Hybrid Feature-based Forgery Detection
  • End-to-end Forgery Detection
  • Feature Fusion-based Forgery Detection
• Network Training
  • Multi-task Learning-based Forgery Detection
• Reference
• Statement

What's New

• [Update Jun. 3, 2025] 🔥🔥🔥 Updated Datasets, Survey, and Top Repositories.
• [Update Feb. 20, 2025] Updated Datasets, Added Survey and Top Repositories.

Survey

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• 【2022-04】-【ADD-Survey】-【Ben Gurion University】
  • A study on data augmentation in voice anti-spoofing
  • Author(s): Ariel Cohen, Inbal Rimon, Eran Aflalo, Haim H. Permuter
  • Paper Code
• 【2022-05】-【ADD-Survey】-【King Saud University】
  • A review of modern audio deepfake detection methods: challenges and future directions
  • Author(s): Zaynab Almutairi, and Hebah Elgibreen
  • Paper
• 【2023-01】-【ADD-Survey】-【University of Maryland Baltimore County】
  • Audio deepfakes: A survey
  • Author(s): Zahra Khanjani, Gabrielle Watson and Vandana P. Janeja
  • Paper
• 【2023-02】-【ADD-Survey】-【Oakland University】
  • Battling voice spoofing: a review, comparative analysis, and generalizability evaluation of state-of-the-art voice spoofing counter measures
  • Author(s): Awais Khan, Khalid Mahmood Malik, James Ryan, and Mikul Saravanan
  • Paper Code
• 【2023-04】-【ADD-Survey】-【Panjab University】
  • Review of audio deepfake detection techniques: Issues and prospects
  • Author(s): Abhishek Dixit, Nirmal Kaur, Staffy Kingra
  • Paper
• 【2023-07】-【ADD-Survey】-【Indian Institute of Technology】
  • Uncovering the deceptions: an analysis on audio spoofing detection and future prospects
  • Author(s): Rishabh Ranjan, Mayank Vatsa, Richa Singh
  • Paper
• 【2023-08】-【ADD-Survey】-【CASIA】
  • Audio deepfake detection: a survey
  • Author(s): Jiangyan Yi, Chenglong Wang, Jianhua Tao, Xiaohui Zhang, Chu Yuan Zhang, and Yan Zhao
  • Paper
• 【2024-02】-【Multimodal-Survey】-【Purdue University, Nanchang University】
  • Detecting Multimedia Generated by Large AI Models: A Survey
  • Author(s): Li Lin, Neeraj Gupta, Yue Zhang, Hainan Ren, Chun-Hao Liu, Feng Ding, Xin Wang, Xin Li, Luisa Verdoliva, Shu Hu
  • Paper Code
• 【2024-04】-【ADD-Survey】-【Toronto Metropolitan University】
  • A Survey on Speech Deepfake Detection
  • Author(s): Menglu Li, Yasaman Ahmadiadli, Xiao-Ping Zhang
  • Paper
• 【2024-05】-【Multimodal-Survey】-【Cool Large Language Models Research Group, Renmin University of China】
  • Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities
  • Author(s): Xiaomin Yua, Yezhaohui Wanga, Yanfang Chenb, Zhen Tao, Dinghao Xi, Shichao Song, and Simin Niu
  • Paper
• 【2024-09】-【ADD-Survey】-【Austrian Institute of Technology】
  • A Comprehensive Survey with Critical Analysis for Deepfake Speech Detection
  • Author(s): Lam Pham, Phat Lam, Tin Nguyen, Hieu Tang, Huyen Nguyen, Alexander Schindler, Hai Canh Vu
  • Paper Code
• 【2024-11】-【Multimodal-Survey】-【University of Bucharest】
  • Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
  • Author(s): Forinel-Alin Croitoru, Andrei-Iulian Hıˆji, Vlad Hondru, Nicolae Cat ̆alin Ristea, Paul Irofti, Marius Popescu, Cristian Rusu, Radu Tudor Ionescu, Fahad Shahbaz Khan, and Mubarak Shah
  • Paper Code
• 【2024-11】-【Multimodal-Survey】-【University College Dublin】
  • Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey
  • Author(s): Hong-Hanh Nguyen-Le, Van-Tuan Tran, Dinh-Thuc Nguyen, Nhien-An Le-Khac
  • Paper
• 【2024-12】-【ADD-Survey】-【Imperial College London, Technical University of Munich】
  • From Audio Deepfake Detection to AI-Generated Music Detection——A Pathway and Overview
  • Author(s): Yupei Li, Manuel Milling, Lucia Specia, Björn W. Schuller
  • Paper
• 【2025-02】-【Multimodal-Survey】-【BUPT, University of California, CASIA】
  • Survey on AI-Generated Media Detection: From Non-MLLM to MLLM
  • Author(s): Yueying Zou, Peipei Li, Zekun Li, Huaibo Huang, Xing Cui, Xuannan Liu, Chenghanyu Zhang, Ran He
  • Paper

Top Repositories

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• Audio Large Language Models
  • Resources on Audio Large Language Models, including datasets, methods, benchmarks, and studies.
  • Repo
• awesome-fake-audio-detection
  • A list of tools, papers and code related to Fake Audio Detection.
  • Repo
• ASVspoof Challenge Official Repository
  • Sub-repositories include ASVspoof 5, ASVspoof 2021, and classifier-adjacency.
  • Repo

Audio Large Model

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Datasets

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Audio Preprocessing

Commonly Used Noise Datasets

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Audio Enhancement Methods

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Feature Extraction

Handcrafted Feature-based Forgery Detection

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Note: "—" indicates not mentioned in the paper. Values in brackets in the experimental results are the ranking of each column in the LA or PA scenario, and bolded values are the best results for that scenario.

Hybrid Feature-based Forgery Detection

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Note: "—" indicates not mentioned in the paper. Values in brackets in the experimental results are the ranking of each column in the LA or PA scenario, and bolded values are the best results for that scenario.

End-to-end Forgery Detection

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Note: "—" indicates not mentioned in the paper. Values in brackets in the experimental results are the ranking of each column in the LA scenario, and bolded values are the best results for that scenario.

Feature Fusion-based Forgery Detection

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Note: "—" indicates not mentioned in the paper. Values in brackets in the experimental results are the ranking of each column in the LA scenario, and bolded values are the best results for that scenario.

Network Training

Multi-task Learning-based Forgery Detection

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Note: "—" indicates not mentioned in the paper. Values in brackets in the experimental results are the ranking of each column in the LA scenario, and bolded values are the best results for that scenario.

Reference

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If you find our repository useful for your research, please cite it as follows:

@article{xu2024,
  title={Research progress on speech deepfake and its detection techniques},
  author={Xu Yuxiong, Li Bin, Tan Shunquan, Huang Jiwu},
  journal={Journal of Image and Graphics},
  volume={29},
  number={8},
  pages={2236--2268},
  year={2024}
}

Statement

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The purpose of this project is to establish a database based on audio deepfake detection, solely for the purpose of communication and learning. All the content collected in this project is sourced from journals and the internet, and we express sincere gratitude to the researchers and authors who have published related research achievements. In the event of a complaint of copyright infringement, the content will be removed as appropriate.