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Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach

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  • Orson Mengara

Abstract

With the growing use of voice-activated systems and speech recognition technologies, the danger of backdoor attacks on audio data has grown significantly. This research looks at a specific type of attack, known as a Stochastic investment-based backdoor attack (MarketBack), in which adversaries strategically manipulate the stylistic properties of audio to fool speech recognition systems. The security and integrity of machine learning models are seriously threatened by backdoor attacks, in order to maintain the reliability of audio applications and systems, the identification of such attacks becomes crucial in the context of audio data. Experimental results demonstrated that MarketBack is feasible to achieve an average attack success rate close to 100% in seven victim models when poisoning less than 1% of the training data.

Suggested Citation

  • Orson Mengara, 2024. "Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach," Papers 2406.10719, arXiv.org, revised Sep 2024.
    • Handle: RePEc:arx:papers:2406.10719
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    References listed on IDEAS

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    1. David Rios Insua & Roi Naveiro & Víctor Gallego & Jason Poulos, 2023. "Adversarial Machine Learning: Bayesian Perspectives," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 2195-2206, July.
    2. L. Z.J. Liang & D. Lemmens & J. Tempere, 2010. "Generalized pricing formulas for stochastic volatility jump diffusion models applied to the exponential Vasicek model," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 75(3), pages 335-342, June.
    3. L. Z. J. Liang & D. Lemmens & J. Tempere, 2010. "Generalized pricing formulas for stochastic volatility jump diffusion models applied to the exponential Vasicek model," Papers 1011.1175, arXiv.org.
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