DeePen: Penetration Testing for Audio Deepfake Detection
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arXiv
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| Format: | Preprint |
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2025
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| author | Müller, Nicolas Kawa, Piotr Stan, Adriana Doan, Thien-Phuc Jung, Souhwan Choong, Wei Herng Sperl, Philip Böttinger, Konstantin |
| author_facet | Müller, Nicolas Kawa, Piotr Stan, Adriana Doan, Thien-Phuc Jung, Souhwan Choong, Wei Herng Sperl, Philip Böttinger, Konstantin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20427 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeePen: Penetration Testing for Audio Deepfake Detection Müller, Nicolas Kawa, Piotr Stan, Adriana Doan, Thien-Phuc Jung, Souhwan Choong, Wei Herng Sperl, Philip Böttinger, Konstantin Cryptography and Security Artificial Intelligence Sound Audio and Speech Processing 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. |
| title | DeePen: Penetration Testing for Audio Deepfake Detection |
| topic | Cryptography and Security Artificial Intelligence Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2502.20427 |