LaserAdv: Laser Adversarial Attacks on Speech Recognition Systems

Audio adversarial perturbations are imperceptible to humans but can mislead machine learning models, posing a security threat to automatic speech recognition (ASR) systems. Existing methods aim to minimize perturbation values, use acoustic masking, or mimic environmental sounds to render them undetectable. However, these perturbations, being audible frequency range sounds, are still audibly detectable. The slow propagation and rapid attenuation of sound limit their temporal sensitivity and attack range. In this study, we propose LaserAdv, a method that employs lasers to launch adversarial attacks, thereby overcoming the aforementioned challenges due to the superior properties of lasers. In the presence of victim speech, laser adversarial perturbations are superimposed on the speech rather than simply drowning it out, so LaserAdv has higher attack efficiency and longer attack range than Light-Commands. LaserAdv introduces a selective amplitude enhancement method based on time-frequency interconversion (SAE-TFI) to deal with distortion. Meanwhile, to simultaneously achieve inaudible, targeted, universal, synchronizationfree (over 0.5 s), long-range, and black-box attacks in the physical world, we introduced a series of strategies into the objective function. Our experimental results show that a single perturbation can cause DeepSpeech, Whisper and iFlytek, to misinterpret any of the 12,260 voice commands as the target command with accuracy of up to 100%, 92% and 88%, respectively. The attack distance can be up to 120 m.