首个开源音频伪造检测与定位工具，支持统一评测与模型对比。
WeDefense: A Toolkit to Defend Against Fake Audio
- 提供统一代码库和标准化评估流程，支持多模型公平比较。
- 涵盖输入增强、分数融合、校准等关键模块，提升检测可靠性。
- 适合研究者、开发者及安全团队快速构建抗伪造系统。
生成式AI的进展使得合成音频在听觉上几乎无法与真实音频区分。尽管这一技术带来诸多积极应用，但也引发冒名顶替、虚假信息和欺诈等风险。尽管已有大量开源检测代码通过竞赛和项目发布，但多数仅针对特定数据集、模型或比赛。目前缺乏一个标准化、统一的工具包，以支持不同解决方案在通用数据库、评测协议、度量标准及共享代码基础上的公平比较。为此，我们提出WeDefense，首个开源工具包，支持假音频检测与定位。除模型训练外，WeDefense强调常被忽视的关键环节：灵活输入与数据增强、模型校准、得分融合、标准化评估指标以及深入分析工具，以提升可解释性。工具包已公开于 https://github.com/zlin0/wedefense，提供交互式演示，支持假音频检测与定位。
原文摘要 · Abstract (English)
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 input and augmentation, calibration, score fusion, standardized evaluation metrics, and analysis tools for deeper understanding and interpretation. The toolkit is publicly available at https://github.com/zlin0/wedefense with interactive demos for fake audio detection and localization.
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