Existing deepfake speech detection systems lack generalizability to unseen attacks (i.e., samples generated by generative algorithms not seen during training). Recent studies have explored the use of universal speech representations to tackle this issue and have obtained inspiring results. These works, however, have focused on innovating downstream classifiers while leaving the representation itself untouched. In this study, we argue that characterizing the long-term temporal dynamics of these representations is crucial for generalizability and propose a new method to assess representation dynamics. Indeed, we show that different generative models generate similar representation dynamics patterns with our proposed method. Experiments on the ASVspoof 2019 and 2021 datasets validate the benefits of the proposed method to detect deepfakes from methods unseen during training, significantly improving on several benchmark methods.
Generalizable Speech Deepfake Detection via Information Bottleneck Enhanced Adversarial Alignment
School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou, China（信息与电子工程学院，浙江工商大学，杭州，中国）；Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, USA（电气与计算机工程系，新泽西理工学院，纽卡斯尔，美国）
BreathNet: Generalizable Audio Deepfake Detection via Breath-Cue-Guided Feature Refinement
Guangdong Key Laboratory of Information Security, School of Computer Science and Engineering, Sun Yat-sen University（广东信息安全重点实验室，计算机科学与工程学院，中山大学）；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）；China Mobile Internet Corporation, China Mobile Communications Corporation（中国移动互联网有限公司，中国移动通信集团）；Guangdong Laboratory of Machine Perception and Intelligent Computing, Faculty of Engineering, Shenzhen MSU-BIT University（广东机器感知与智能计算实验室，工程学院，深圳MSU-BIT大学）
A Data-Centric Approach to Generalizable Speech Deepfake Detection
Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence, AI Institute School of Computer Science, Shanghai Jiao Tong University, China（听觉认知与计算声学实验室、人工智能MoE重点实验室、人工智能学院、上海交通大学）
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（弗劳恩霍夫应用安全研究中心）；Technical University of Munich（慕尼黑工业大学）；University of Southampton（南安普顿大学）
Deepfakes are synthetically generated media often devised with malicious intent. They have become increasingly more convincing with large training datasets advanced neural networks. These fakes are readily being misused for slander, misinformation and fraud. For this reason, intensive research for developing countermeasures is also expanding. However, recent work is almost exclusively limited to deepfake detection - predicting if audio is real or fake. This is despite the fact that attribution (who created which fake?) is an essential building block of a larger defense strategy, as practiced i…
RTCFake: Speech Deepfake Detection in Real-Time Communication
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education（航空航天信息安全部与可信计算教育部重点实验室）；School of Cyber Science and Engineering, Wuhan University（武汉大学计算机科学与工程学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Beihang University（北京航空航天大学）
Textual Acoustic Grounding for Generalizable LLM-Based Deepfake Voice Detection
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；National Institute of Informatics（情报信息学研究所）；Technical University of Berlin（柏林工业大学）
Imperial College London（伦敦帝国学院）；Chinese University HongKong, Shenzhen, China（香港中文大学（深圳））；Huawei, Singapore（新加坡华为）；Technical University Munich, Munich, German（慕尼黑技术大学）
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）
