WeDefense: A Toolkit to Defend Against Fake Audio

2026-01-21

AI Summary

This work addresses the absence of a unified, standardized open-source evaluation framework in the field of forged audio detection and localization, which has hindered fair comparison and in-depth analysis among existing methods. To bridge this gap, we introduce the first open-source toolkit that simultaneously supports both detection and localization tasks. Designed with modularity, the toolkit integrates flexible input processing, data augmentation, model calibration, multi-model score fusion, standardized evaluation metrics, and interpretable visualization capabilities. It establishes, for the first time, a consistent data protocol and evaluation pipeline, thereby filling a critical void in standardized infrastructure for the community. By releasing open-source code and an interactive demo, this work enables researchers to develop, evaluate, and compare anti-forgery audio defense techniques within a common, reproducible framework.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Responsible Web: Audits of web technologies, standards, platforms, and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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 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.

Problem

• fake audio detection
• audio forensics
• benchmarking
• generative AI
• toolkit

Innovation

• fake audio detection
• audio forensics
• open-source toolkit
• score fusion
• standardized evaluation