A Black-Box Speech Adversarial Attack Method Based on Enhanced Neural Predictors in Industrial IoT

Devices in Industrial Internet of Things are vulnerable to voice adversarial attacks. Studying adversarial speech samples is crucial for enhancing the security of automatic speech recognition systems in Industrial Internet of Things devices. Current black-box attack methods often face challenges such as complex search processes and excessive perturbation generation. To address these issues, this paper proposes a black-box voice adversarial attack method based on enhanced neural predictors. This method searches for minimal perturbations in the perturbation space, employing an optimization process guided by a self-attention neural predictor to identify the optimal perturbation direction. This direction is then applied to the original sample to generate adversarial samples. To improve search efficiency, a pruning strategy is designed to discard samples below a threshold in the early search stages, reducing the number of searches. Additionally, a dynamic factor based on feedback from querying the automatic speech recognition system is introduced to adaptively adjust the search step size, further accelerating the search process. To validate the performance of the proposed method, experiments are conducted on the LibriSpeech dataset. Compared with the mainstream methods, the proposed method improves the signal-to-noise ratio by 0.8 dB, increases sample similarity by 0.43%, and reduces the average number of queries by 7%. Experimental results demonstrate that the proposed method offers better attack effectiveness and stealthiness.