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Hello Me, Meet the Real Me: Audio Deepfake Attacks on Voice Assistants

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arxiv 2302.10328 v1 pith:DDSFPKY6 submitted 2023-02-20 cs.CR

classification cs.CR
keywords attacksparticipantsusedvoiceassistantscountermeasuresdangerousdeepfake
verification ladder T0 review T1 audit T2 compute T3 formal
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The radical advances in telecommunications and computer science have enabled a myriad of applications and novel seamless interaction with computing interfaces. Voice Assistants (VAs) have become a norm for smartphones, and millions of VAs incorporated in smart devices are used to control these devices in the smart home context. Previous research has shown that they are prone to attacks, leading vendors to countermeasures. One of these measures is to allow only a specific individual, the device's owner, to perform possibly dangerous tasks, that is, tasks that may disclose personal information, involve monetary transactions etc. To understand the extent to which VAs provide the necessary protection to their users, we experimented with two of the most widely used VAs, which the participants trained. We then utilised voice synthesis using samples provided by participants to synthesise commands that were used to trigger the corresponding VA and perform a dangerous task. Our extensive results showed that more than 30\% of our deepfake attacks were successful and that there was at least one successful attack for more than half of the participants. Moreover, they illustrate statistically significant variation among vendors and, in one case, even gender bias. The outcomes are rather alarming and require the deployment of further countermeasures to prevent exploitation, as the number of VAs in use is currently comparable to the world population.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

    cs.CR 2026-03 unverdicted novelty 6.0 of 10

    The survey organizes over 400 papers on embodied AI safety into a multi-level taxonomy and flags overlooked issues such as fragile multimodal fusion and unstable planning under jailbreaks.

  2. Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

    cs.CR 2026-03 accept novelty 6.0 of 10

    A multi-level taxonomy of risks, attacks, and defenses across the full embodied AI pipeline, synthesizing 500+ papers and flagging overlooked failure modes.

  3. XAttnMark: Learning Robust Audio Watermarking with Cross-Attention

    cs.SD 2025-02 unverdicted novelty 5.0 of 10

    XAttnMark is a new neural audio watermarking method using partial parameter sharing, cross-attention for message retrieval, temporal conditioning, and a psychoacoustic TF masking loss that reports state-of-the-art det...

  4. Security and Privacy in Virtual and Robotic Assistive Systems: A Comparative Framework

    cs.CR 2026-03 unverdicted novelty 4.0 of 10

    A unified comparative threat-modeling framework is developed to analyze security and privacy risks across virtual and robotic assistive systems.

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