Boosting Imperceptibility of Adversarial Attacks for Environmental Sound Classification

Shaojian Qiu, Xiaokang You, Wei Rong, Lifeng Huang, Yun Liang

Abstract: As artificial intelligence (AI) continues to advance, AI-based audio systems are becoming increasingly vulnerable to adversarial attacks. However, most current studies overlook the scenes of environmental sounds and the imperceptibility of attack. In response to these, we propose a novel frequency-weighted perturbation algorithm for environmental sounds called the Frequency Psychological Attack Algorithm (FPAA). This innovative algorithm incorporates auditory thresholds with psychoacoustic principles during the perturbation generation process to create highly imperceptible adversarial examples. Extensive experiments conducted on two public datasets using multiple models demonstrate that our FPAA algorithm can produce adversarial audio examples that are not only imperceptible to the human ear but also maintain high offensive capability against AI-based audio systems.