PASK: Sparse Framework for Crafting Natural Adversarial Example

As audio adversarial attacks continue to evolve, Automatic Speech Recognition (ASR) models have emerged as a significant target. Traditional audio attack methods often focus on minimizing perturbation magnitude and frequency, overlooking the importance of perturbation location. However, certain audio regions hold lower importance for ASR models, making attacks on these regions less effective and more perceptible as noise. Additionally, the human ear perceives noise differently depending on its placement within the audio sequence, with noise in silent segments being more noticeable. To address these challenges, this paper proposes Pitch Sparse Audio Attack (PASK), an innovative framework designed to enhance adversarial imperceptibility through sparse perturbations. PASK introduces two key techniques: Pitch Mapping, which provides a strategic starting point for perturbation, and an adaptive grouped selective mask that achieves targeted sparsity, focusing perturbations on high-impact audio regions. Experimental results demonstrate that PASK outperforms existing methods in both effectiveness and imperceptibility. Furthermore, a human study confirms that silent-segment perturbations are more easily detected, underscoring the perceptual advantages of our approach.