Weighted-Sampling Audio Adversarial Example Attack
This addresses security vulnerabilities in speech recognition systems, though it appears incremental as it builds on existing adversarial example research.
The paper tackles the problem of generating efficient and robust audio adversarial examples for automatic speech recognition systems by proposing a weighted-sampling method with denoising, achieving low noise and high robustness with minimal time consumption.
Recent studies have highlighted audio adversarial examples as a ubiquitous threat to state-of-the-art automatic speech recognition systems. Thorough studies on how to effectively generate adversarial examples are essential to prevent potential attacks. Despite many research on this, the efficiency and the robustness of existing works are not yet satisfactory. In this paper, we propose~\textit{weighted-sampling audio adversarial examples}, focusing on the numbers and the weights of distortion to reinforce the attack. Further, we apply a denoising method in the loss function to make the adversarial attack more imperceptible. Experiments show that our method is the first in the field to generate audio adversarial examples with low noise and high audio robustness at the minute time-consuming level.