Title: SirenAttack: Generating Adversarial Audio for End-to-End Acoustic Systems,
Abstract: Despite their immense popularity, deep learning-based acoustic systems are
inherently vulnerable to adversarial attacks, wherein maliciously crafted
audios trigger target systems to misbehave. In this paper, we present
SirenAttack, a new class of attacks to generate adversarial audios. Compared
with existing attacks, SirenAttack highlights with a set of significant
features: (i) versatile -- it is able to deceive a range of end-to-end acoustic
systems under both white-box and black-box settings; (ii) effective -- it is
able to generate adversarial audios that can be recognized as specific phrases
by target acoustic systems; and (iii) stealthy -- it is able to generate
adversarial audios indistinguishable from their benign counterparts to human
perception. We empirically evaluate SirenAttack on a set of state-of-the-art
deep learning-based acoustic systems (including speech command recognition,
speaker recognition and sound event classification), with results showing the
versatility, effectiveness, and stealthiness of SirenAttack. For instance, it
achieves 99.45% attack success rate on the IEMOCAP dataset against the ResNet18
model, while the generated adversarial audios are also misinterpreted by
multiple popular ASR platforms, including Google Cloud Speech, Microsoft Bing
Voice, and IBM Speech-to-Text. We further evaluate three potential defense
methods to mitigate such attacks, including adversarial training, audio
downsampling, and moving average filtering, which leads to promising directions
for further research.