This paper describes our submitted systems to the 2022 ADD challenge withing the tracks 1 and 2. Our approach is based on the combination of a pre-trained wav2vec2 feature extractor and a downstream classifier to detect spoofed audio. This method exploits the contextualized speech representations at the different transformer layers to fully capture discriminative information. Furthermore, the classification model is adapted to the application scenario using different data augmentation techniques. We evaluate our system for audio synthesis detection in both the ASVspoof 2021 and the 2022 ADD challenges, showing its robustness and good performance in realistic challenging environments such as telephonic and audio codec systems, noisy audio, and partial deepfakes.
ADD 2023: the Second Audio Deepfake Detection Challenge
Peoples' Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Joint Institute for Nuclear Research（联合核研究所）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；University of Skövde（舍夫德大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）；The Chinese University of Hong Kong（香港中文大学）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；University of Science and Technology of China（中国科学技术大学）；Beijing Jiaotong University（北京交通大学）；Hebei University of Technology（河北工业大学）
A Comprehensive Survey with Critical Analysis for Deepfake Speech Detection
Austrian Institute of Technology（奥地利技术研究院）；HCM University of Technology（胡志明市理工大学）；University of Technology of Troyes（特鲁瓦技术大学）；FPT University（FPT大学）；BLOOM Social Analytics（BLOOM社会分析公司）；Université de technologie de Compiègne（贡比涅技术大学）
Audio Deepfake Attribution: An Initial Dataset and Investigation
School of Electronics and Information Engineering, Beihang University（北京航空航天大学电子信息工程学院）；Tsinghua University（清华大学）；National Research Center for Information Science and Technology, Tsinghua University（清华大学国家信息科学与技术研究中心）
AT-ADD: A Benchmark and Challenge for Robust and All-Type Audio Deepfake Detection
Communication University of China（中国传媒大学）；Ant Group（蚂蚁集团）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Institute of Technology（北京理工大学）；Shanghai Jiao Tong University（上海交通大学）
Communication University of China（中国传媒大学）；Ant Group（蚂蚁集团）；Machine Intelligence, Ant Group（蚂蚁集团机器智能）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Institute of Technology（北京理工大学）
本文总结了ACM MM 2026 AT-ADD全类型音频深度伪造检测挑战赛的两个赛道任务、设计、结果及参赛系统的常见模式，指出当前仍存在泛化、鲁棒性及性能均衡等挑战。
AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
Communication University of China（中国传媒大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beijing Institute of Technology（北京理工大学）；Shanghai Jiao Tong University（上海交通大学）
Manipulated Regions Localization For Partially Deepfake Audio: A Survey
State Key Laboratory of Multi-modal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Department of Automation, Tsinghua University（自动化系，清华大学）；Department of Automation and Beijing National Research Center for Information Science and Technology, Tsinghua University（自动化系和北京信息科学与技术国家研究中心，清华大学）；University of Chinese Academy of Sciences（中国科学院大学）
Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Information and Communication Engineering, Communication University of China（中国传媒大学信息与通信工程学院）；Department of Automation, Tsinghua University（清华大学自动化系）；Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学北京信息科学与技术国家研究中心）；Shanghai Polytechnic University（上海第二工业大学）
Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook
Department of Computer Science, University of Bucharest（布加勒斯特大学计算机科学系）；Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（穆罕默德·本·扎耶德人工智能大学）；Department of Computer Science, University of Central Florida（中佛罗里达大学计算机科学系）
