Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech in Real Time
Xinfeng Li, Chen Yan, Xuancun Lu, Zihan Zeng, Xiaoyu Ji, Wenyuan Xu

TL;DR
This paper introduces VRIFLE, an ultrasonic inaudible adversarial attack that manipulates speech recognition in real time, even in user-present scenarios, by overcoming physical and environmental challenges.
Contribution
The paper presents VRIFLE, a novel ultrasonic attack method with a transformation model and robustness enhancements for real-time, physical-world speech recognition manipulation.
Findings
VRIFLE effectively manipulates ASR outputs in real-time.
It remains robust against six defense mechanisms.
The attack works with portable devices and everyday loudspeakers.
Abstract
Automatic speech recognition (ASR) systems have been shown to be vulnerable to adversarial examples (AEs). Recent success all assumes that users will not notice or disrupt the attack process despite the existence of music/noise-like sounds and spontaneous responses from voice assistants. Nonetheless, in practical user-present scenarios, user awareness may nullify existing attack attempts that launch unexpected sounds or ASR usage. In this paper, we seek to bridge the gap in existing research and extend the attack to user-present scenarios. We propose VRIFLE, an inaudible adversarial perturbation (IAP) attack via ultrasound delivery that can manipulate ASRs as a user speaks. The inherent differences between audible sounds and ultrasounds make IAP delivery face unprecedented challenges such as distortion, noise, and instability. In this regard, we design a novel ultrasonic transformation…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning
