An Initial Investigation of Neural Replay Simulator for Over-the-Air Adversarial Perturbations to Automatic Speaker Verification
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)(2023)
摘要
Deep Learning has advanced Automatic Speaker Verification (ASV) in the past
few years. Although it is known that deep learning-based ASV systems are
vulnerable to adversarial examples in digital access, there are few studies on
adversarial attacks in the context of physical access, where a replay process
(i.e., over the air) is involved. An over-the-air attack involves a
loudspeaker, a microphone, and a replaying environment that impacts the
movement of the sound wave. Our initial experiment confirms that the replay
process impacts the effectiveness of the over-the-air attack performance. This
study performs an initial investigation towards utilizing a neural replay
simulator to improve over-the-air adversarial attack robustness. This is
achieved by using a neural waveform synthesizer to simulate the replay process
when estimating the adversarial perturbations. Experiments conducted on the
ASVspoof2019 dataset confirm that the neural replay simulator can considerably
increase the success rates of over-the-air adversarial attacks. This raises the
concern for adversarial attacks on speaker verification in physical access
applications.
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关键词
Speaker verification,adversarial attack,replay attack,over-the-air attack,replay simulation
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