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arXiv · 2502.20427

DeePen: Penetration Testing for Audio Deepfake Detection

Nicolas Müller·Piotr Kawa·Adriana Stan·Thien-Phuc Doan·Souhwan Jung·Wei Herng Choong·Philip Sperl·Konstantin Böttinger
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Abstract

Deepfakes - manipulated or forged audio and video media - pose significant security risks to individuals, organizations, and society at large. To address these challenges, machine learning-based classifiers are commonly employed to detect deepfake content. In this paper, we assess the robustness of such classifiers through a systematic penetration testing methodology, which we introduce as DeePen. Our approach operates without prior knowledge of or access to the target deepfake detection models. Instead, it leverages a set of carefully selected signal processing modifications - referred to as attacks - to evaluate model vulnerabilities. Using DeePen, we analyze both real-world production systems and publicly available academic model checkpoints, demonstrating that all tested systems exhibit weaknesses and can be reliably deceived by simple manipulations such as time-stretching or echo addition. Furthermore, our findings reveal that while some attacks can be mitigated by retraining detection systems with knowledge of the specific attack, others remain persistently effective.

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Nicolas Müller, Piotr Kawa, Adriana Stan, Thien-Phuc Doan, Souhwan Jung, Wei Herng Choong, Philip Sperl, Konstantin Böttinger. 2025-02-27. DeePen: Penetration Testing for Audio Deepfake Detection. https://arxiv.org/abs/2502.20427

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