arXiv:2509.21597v2 Announce Type: replace-cross Abstract: With the prevalence of artificial intelligence (AI)-generated content, such as audio deepfakes, a large body of recent work has focused on developing deepfake detection techniques. However, existing benchmarks employ a narrow set of datasets, leaving detector generalization to real-world conditions uncertain. In this paper, we systematically review 31 existing audio deepfake datasets and present an open-source benchmarking toolkit called AUDDT (https://github.com/MuSAELab/AUDDT). The goal of this toolkit is to automate the evaluation of
The proliferation of AI-generated content, specifically audio deepfakes, necessitates robust detection mechanisms as the technology becomes more sophisticated and accessible.
This development addresses a critical vulnerability in trust and information integrity, with implications for everything from cybersecurity to election security and identity verification.
The introduction of a unified benchmarking toolkit standardizes evaluation of audio deepfake detectors, improving the reliability and generalizability of protective technologies.
- · Cybersecurity firms
- · AI ethicists
- · Law enforcement
- · Speech technology companies
- · Deepfake creators
- · Disinformation actors
- · Organizations with weak security protocols
Improved detection capabilities will make it harder for malicious actors to deploy effective audio deepfakes.
Increased trust in audio communications and biometric authentication systems, mitigating some risks associated with advanced AI capabilities.
Potential for new regulatory frameworks and industry standards around AI-generated content and its authentication.
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Read at arXiv cs.CL