Semantic Web-Driven Targeted Adversarial Attack on Black Box Automatic Speech Recognition Systems

Abstract

The susceptibility of Deep Neural Networks (DNNs) to adversarial attacks in Automatic Speech Recognition (ASR) systems has drawn significant attention. Most work focuses on white-box methods, but the assumption of full transparency of model architecture and parameters is unrealistic in real-world scenarios. Although several targeted black-box attack methods have been proposed in recent years, due to the complexity of ASR systems, they primarily rely on query-based approaches with limited search capabilities, leading to low success rates and noticeable noise. To address this, we propose DE-gradient, a new black-box approach using differential evolution (DE), a population-based search algorithm. Inspired by Semantic Web ideas, we introduce modulation noise to preserve semantic coherence while enhancing imperceptibility. In experiments on two public datasets, DE-gradient improved attack success rates by 19% and increased the signal-to-noise ratio (SNR) of silent parts from 27 dB to 54 dB, establishing a strong baseline for evaluating black-box adversarial attacks in ASR systems.