Machine Learning Voice Synthesis for Intention Electromagnetic Interference Injection in Smart Speaker Devices
This work presents the effectiveness of using machine learning (ML) synthesized voice samples to control smart speaker devices through radiated intentional electromagnetic interference (I-EMI). In previous works, the feasibility of using I-EMI to control smart speaker devices was shown. However, devices that are trained to only recognize a single person's voice or only execute certain commands from that person will not be as susceptible to this attack. By training a generative adversarial network (GAN) using samples of the target's voice, this security feature can be bypassed directly, increasing the feasibility of the attack.
T. Fokkens et al., "Machine Learning Voice Synthesis for Intention Electromagnetic Interference Injection in Smart Speaker Devices," Proceedings of the 2021 Joint IEEE International Symposium on EMC/SI/PI, and EMC Europe (2021, Raleigh, NC), pp. 673-677, Institute of Electrical and Electronics Engineers (IEEE), Aug 2021.
The definitive version is available at https://doi.org/10.1109/EMC/SI/PI/EMCEurope52599.2021.9559146
2021 IEEE International Joint Electromagnetic Compatibility Signal and Power Integrity and EMC Europe Symposium, EMC/SI/PI/EMC Europe 2021 (2021: Jul. 26-Aug. 13, Raleigh, NC)
Electrical and Computer Engineering
Electromagnetic Compatibility (EMC) Laboratory
Keywords and Phrases
Electromagnetic Interference (EMI); Generative Adversarial Networks (GAN); Internet of Things (IoT); Machine Learning (ML)
International Standard Book Number (ISBN)
Article - Conference proceedings
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13 Aug 2021