Defending against Adversarial Audio via Diffusion Model. (arXiv:2303.01507v1 [cs.SD])

Deep learning models have been widely used in commercial acoustic systems in
recent years. However, adversarial audio examples can cause abnormal behaviors
for those acoustic systems, while being hard for humans to perceive. Various
methods, such as transformation-based defenses and adversarial training, have
been proposed to protect acoustic systems from adversarial attacks, but they
are less effective against adaptive attacks. Furthermore, directly applying the
methods from the image domain can lead to suboptimal results because of the
unique properties of …

acoustic adversarial attacks audio commercial deep learning domain hard humans protect systems training transformation