The First Environmental Sound Deepfake Detection Challenge: Benchmarking Robustness, Evaluation, and Insights
School of Electrical Engineering, KAIST, Daejeon, Republic of Korea（韩国成均馆大学电气工程学院）；University of Melbourne, Australia（墨尔本大学）；Fortemedia Singapore, Singapore（新加坡Fortemedia公司）；Xi’an University of Posts & Telecommunications, Xi’an, China（西安邮电大学）；Xi'an Lianfeng Acoustic Technologies Co., Ltd., China（西安联丰声学技术有限公司）
Han Yin, Yang Xiao, Rohan Kumar Das, Jisheng Bai, Ting Dang
Recent progress in audio generation has made it increasingly easy to create highly realistic environmental soundscapes, which can be misused to produce deceptive content, such as fake alarms, gunshots, and crowd sounds, raising concerns for public safety and trust. While deepfake detection for speech and singing voice has been extensively studied, environmental sound deepfake detection (ESDD) remains underexplored. To advance ESDD, the first edition of the ESDD challenge was launched, attracting 97 registered teams and receiving 1,748 valid submissions. This paper presents the task formulation, dataset construction, evaluation protocols, baseline systems, and key insights from the challenge results. Furthermore, we analyze common architectural choices and training strategies among top-performing systems. Finally, we discuss potential future research directions for ESDD, outlining key opportunities and open problems to guide subsequent studies in this field.
ESDD 2026: Environmental Sound Deepfake Detection Challenge Evaluation Plan
1 School of Electrical Engineering, KAIST, Daejeon, Republic of Korea 2 University of Melbourne, Australia 3 Fortemedia Singapore, Singapore 4 Xi'an University of Posts \& Telecommunications, Xi'an, China
Environmental Sound Deepfake Detection Challenge: An Overview
1 School of Electrical Engineering, KAIST, Daejeon, Republic of Korea 2 University of Melbourne, Australia, 3 Fortemedia Singapore, Singapore, 4 Xi’an University of Posts \& Telecommunications, Xi’an, China, 5 Xi'an Lianfeng Acoustic Technologies Co., Ltd., China
Overview of ESDD2: Environment-Aware Speech and Sound Deepfake Detection Challenge
Duke Kunshan University（昆山杜克大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；The University of Melbourne（墨尔本大学）；Johns Hopkins University（约翰霍普金斯大学）；Fortemedia Singapore（Fortemedia新加坡）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Environmental Sound Deepfake Detection Using Deep-Learning Framework
Center for Digital Safety & Security, Austrian Institute of Technology (AIT)（数字安全与安全中心，奥地利技术研究所）；FPT University（FPT大学）；Ton Duc Thang University（-ton Duc Thang大学）；HCM University of Technology（胡志明理工大学）
ESDD2: Environment-Aware Speech and Sound Deepfake Detection Challenge Evaluation Plan
Duke Kunshan University（杜克-肯斯敦大学）；Korea Advanced Institute of Science and Technology（韩国科学技术院）；The University of Melbourne（墨尔本大学）；Johns Hopkins University（约翰霍普金斯大学）；Fortemedia Singapore（新加坡Fortemedia）
MixFake: Benchmarking and Enhancing Audio Deepfake Detection in Diverse Real-world Mixed Audio
School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing, China（南京理工大学信息科学与工程学院）；The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China（浙江大学区块链与数据安全国家重点实验室）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China（杭州高新技术区（滨江）区块链与数据安全研究院）
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）
EnvSSLAM-FFN: Lightweight Layer-Fused System for ESDD 2026 Challenge
1State Key Laboratory of Media Convergence；Communication, Communication University of China, Beijing, China 2Machine Intelligence, Ant Group, Shanghai, China 3Key Laboratory of Media Audio \& Video, Ministry of Education Communication University of China, Beijing, China
École de Technologie Supérieure (ÉTS), University of Quebec（魁北克大学高等技术学院）
In this paper, we investigate the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network. Averaged over various experiments on three benchmarking environmental sound datasets, we found the ResNet-18 model outperforms other deep learning architectures such as GoogLeNet and AlexNet both in terms of classification accuracy and the number of training parameters. Therefore we set this model as our front-end classifier for subsequent investigations. Herein, we m…
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）
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。
RealDESED: A Real-World Domestic Sound Event Detection Benchmark
Institute of Computational Perception, Johannes Kepler University Linz, Austria（计算感知研究所，约翰尼斯·开普勒大学林茨，奥地利）；LIT Artificial Intelligence Lab, Linz, Austria（LIT人工智能实验室，林茨，奥地利）
École de Technologie Supérieure (ÉTS)（高等技术学院）；Université du Québec（魁北克大学）
This paper investigates the impact of different standard environmental sound representations (spectrograms) on the recognition performance and adversarial attack robustness of a victim residual convolutional neural network, namely ResNet-18. Our main motivation for focusing on such a front-end classifier rather than other complex architectures is balancing recognition accuracy and the total number of training parameters. Herein, we measure the impact of different settings required for generating more informative Mel-frequency cepstral coefficient (MFCC), short-time Fourier transform (STFT), an…
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）
DeepFense: A Unified, Modular, and Extensible Framework for Robust Deepfake Audio Detection
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；University of Stuttgart（斯图加特大学）；National Institute of Informatics（国立信息学研究所）；Technical University of Berlin（柏林工业大学）
ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学及其应用实验室）；National Institute of Informatics（国立信息学研究所）；Institute for Advancing Intelligence, TCG CREST（TCG CREST 智能推进研究所）；EURECOM（欧洲通信学院）；Cerence Inc.（赛轮思公司）；Avignon Université（阿维尼翁大学）；Nuance Communications（Nuance 通信公司）；University of Eastern Finland（东芬兰大学）
University of Southampton（南安普顿大学）；Friedrich-Alexander University Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Imperial College London（帝国理工学院）
The ability to localize and track acoustic events is a fundamental prerequisite for equipping machines with the ability to be aware of and engage with humans in their surrounding environment. However, in realistic scenarios, audio signals are adversely affected by reverberation, noise, interference, and periods of speech inactivity. In dynamic scenarios, where the sources and microphone platforms may be moving, the signals are additionally affected by variations in the source-sensor geometries. In practice, approaches to sound source localization and tracking are often impeded by missing estim…
National Taiwan University（台湾大学）；Far Eastern Memorial Hospital（远东纪念医院）；Heroic Faith Medical Science Co. Ltd.（浩信医疗科技股份有限公司）；Avalanche Computing Inc.（雪崩计算公司）；Academia Sinica（中央研究院）；HCC Healthcare Group（HCC医疗集团）
A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalati…
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）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, Chinese Academy of Sciences（中国科学院人工智能学院）；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）；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）；Department of Automation, Tsinghua University（清华大学自动化系）；School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University（安徽省多模态认知计算重点实验室，安徽大学计算机科学与技术学院）；Tencent, AI Lab（腾讯，AI实验室）
RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios
Beijing University of Posts and Telecommunications（北京邮电大学）；Nanyang Technological University（南洋理工大学）；Xidian University（西安电子科技大学）；Shanghai University（上海大学）
BEAT2AASIST model with layer fusion for ESDD 2026 Challenge
Department of Mathematics, Korea University（韩国大学数学系）；Department of Statistics and Data Science, Chung-Ang University（Chung-Ang 大学统计与数据科学系）；Department of Smart Cities, Chung-Ang University（Chung-Ang 大学智慧城市系）；Center for Applied Mathematics, Cornell University（康奈尔大学应用数学中心）；Department of Big Data Application, Hannam University（汉南大学大数据应用系）
