Self-supervised learning (SSL) has transformed speech processing, with benchmarks such as SUPERB establishing fair comparisons across diverse downstream tasks. Despite it's security-critical importance, Audio deepfake detection has remained outside these efforts. In this work, we introduce Spoof-SUPERB, a benchmark for audio deepfake detection that systematically evaluates 20 SSL models spanning generative, discriminative, and spectrogram-based architectures. We evaluated these models on multiple in-domain and out-of-domain datasets. Our results reveal that large-scale discriminative models such as XLS-R, UniSpeech-SAT, and WavLM Large consistently outperform other models, benefiting from multilingual pretraining, speaker-aware objectives, and model scale. We further analyze the robustness of these models under acoustic degradations, showing that generative approaches degrade sharply, while discriminative models remain resilient. This benchmark establishes a reproducible baseline and provides practical insights into which SSL representations are most reliable for securing speech systems against audio deepfakes.
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）
Hindi audio-video-Deepfake (HAV-DF): A Hindi language-based Audio-video Deepfake Dataset
BML Munjal University（BML蒙贾尔大学）；School of Engineering & Technology, BML Munjal University（BML蒙贾尔大学工程与技术学院）；Center for Advanced Data and Computational Science, BML Munjal University（BML蒙贾尔大学高级数据与计算科学中心）
The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audio
State Key Laboratory of Media Convergence and Communication（媒体融合与传播国家重点实验室）；School of Data Science and Intelligent Media, Communication University of China（中国传媒大学数据科学与智能媒体学院）；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）；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）；School of Cyberspace science and Technology, Beijing Institute of Technology（北京理工大学网络空间科学与技术学院）
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）
Fraunhofer AISEC（弗劳恩霍夫人工智能安全研究所）；Wrocław University of Science and Technology（沃斯克拉大学科学与技术学院）；Resemble AI（Resemble AI公司）；Technical University of Cluj-Napoca（克鲁克斯-纳波卡技术大学）；Neodyme AG（Neodyme公司）；TU Munich（慕尼黑技术大学）
AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection
Social Networks and Human-Centered Computing Program, Taiwan International Graduate Program, Academia Sinica（社会网络与以人为本计算计划、台湾国际研究生计划、中国科学院）；Institute of Information Systems and Applications, National Tsing Hua University（信息系统与应用研究所、国立清华大学）；Department of Computer Science, National Chengchi University（计算机科学系、国立成功大学）；Research Center for Information Technology Innovation, Academia Sinica（信息技术创新研究中心、中国科学院）；Institute of Information Science, Academia Sinica（信息科学研究所、中国科学院）
Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features
Anhui University（安徽大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Institute of Acoustics, Chinese Academy of Sciences（中国科学院声学研究所）；Qiyuan Laboratory（启元实验室）
University of Bucharest（布加勒斯特大学）；MBZ University of Artificial Intelligence（MBZ人工智能大学）；Linköping University（利德大学）；University of Central Florida（佛罗里达中央大学）
Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning
Department of CSE, BRAC University, Bangladesh（布鲁斯大学计算机科学与工程系）；Department of CSE, Ahsanullah University of Science and Technology, Bangladesh（阿沙努拉科技大学计算机科学与工程系）；Enosis Solutions, Bangladesh（恩奥斯解决方案公司）；Department of CSE, Southeast University, Bangladesh（东南大学计算机科学与工程系）
From Talking to Singing: A New Challenge for Audio-Visual Deepfake Detection
Center for Future Media, School of Computer Science and Engineering, University of Electronic Science and Technology of China（未来媒体中心，计算机科学与工程学院，电子科技大学）
Does Current Deepfake Audio Detection Model Effectively Detect ALM-based Deepfake Audio?
Communication University of China（中国传媒大学）；SDU-ANU Joint Science College, Shandong University（山东大学-澳大利亚国立大学联合科学学院）；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）；Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology, Tsinghua University（北京信息科学与技术国家研究中心（清华大学））
Targeted Augmented Data for Audio Deepfake Detection
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg（卢森堡大学跨学科安全、可靠性与信任中心（SnT））；Cristal Laboratory, National School of Computer Sciences (ENSI), Manouba University（马努巴大学计算机科学国家学院（ENSI）克里斯塔尔实验室）
Generalized Source Tracing: Detecting Novel Audio Deepfake Algorithm with Real Emphasis and Fake Dispersion Strategy
Communication University of China（中国传媒大学）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；School of Computer and Information Technology, University of Beijing Jiaotong（北京交通大学计算机与信息技术学院）；Department of Automation, Tsinghua University（清华大学自动化系）；University of Science and Technology of China（中国科学技术大学）
