WeDefense: A Toolkit to Defend Against Fake Audio
Lin Zhang, Johan Rohdin, Xin Wang, Junyi Peng, Tianchi Liu, You Zhang, Hieu-Thi Luong, Shuai Wang, Chengdong Liang, Anna Silnova, Nicholas Evans

TL;DR
WeDefense is an open-source toolkit designed to standardize and facilitate the benchmarking, detection, and localization of fake audio generated by AI, supporting fair comparison of different solutions.
Contribution
It introduces the first unified toolkit for fake audio detection and localization, including comprehensive features like augmentation, calibration, and analysis tools.
Findings
Supports both detection and localization of fake audio
Provides standardized evaluation metrics and protocols
Includes interactive demos for practical use
Abstract
The advances in generative AI have enabled the creation of synthetic audio which is perceptually indistinguishable from real, genuine audio. Although this stellar progress enables many positive applications, it also raises risks of misuse, such as for impersonation, disinformation and fraud. Despite a growing number of open-source fake audio detection codes released through numerous challenges and initiatives, most are tailored to specific competitions, datasets or models. A standardized and unified toolkit that supports the fair benchmarking and comparison of competing solutions with not just common databases, protocols, metrics, but also a shared codebase, is missing. To address this, we propose WeDefense, the first open-source toolkit to support both fake audio detection and localization. Beyond model training, WeDefense emphasizes critical yet often overlooked components: flexible…
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Taxonomy
TopicsMusic and Audio Processing · Digital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis
