  Automatic speech recognition (ASR) systems are vulnerable to audio
adversarial examples that attempt to deceive ASR systems by adding
perturbations to benign speech signals. Although an adversarial example and the
original benign wave are indistinguishable to humans, the former is transcribed
as a malicious target sentence by ASR systems. Several methods have been
proposed to generate audio adversarial examples and feed them directly into the
ASR system (over-line). Furthermore, many researchers have demonstrated the
feasibility of robust physical audio adversarial examples(over-air). To defend
against the attacks, several studies have been proposed. However, deploying
them in a real-world situation is difficult because of accuracy drop or time
overhead. In this paper, we propose a novel method to detect audio adversarial
examples by adding noise to the logits before feeding them into the decoder of
the ASR. We show that carefully selected noise can significantly impact the
transcription results of the audio adversarial examples, whereas it has minimal
impact on the transcription results of benign audio waves. Based on this
characteristic, we detect audio adversarial examples by comparing the
transcription altered by logit noising with its original transcription. The
proposed method can be easily applied to ASR systems without any structural
changes or additional training. The experimental results show that the proposed
method is robust to over-line audio adversarial examples as well as over-air
audio adversarial examples compared with state-of-the-art detection methods.
