CBA: Backdoor Attack on Deep Speech Classification via Audio Compression

As deep neural networks permeate various aspects of society, research has demonstrated their vulnerability to backdoor attacks, particularly with untrustworthy third-party platforms, enabling attackers to manipulate model outputs using customized triggers. However, most audio-based backdoor methods are either sample-agnostic or audible. In this paper, we propose Compression-based Backdoor Attack (CBA), a novel strategy designed specifically for audio compression scenarios, which also utilizes fusion as well as iterative optimization strategies to generate sample-specific triggers and ensure both the effectiveness and stealthiness of the triggers. Extensive experiments validate the attack efficiency and robustness.