Characterizing Audio Adversarial Examples Using Temporal Dependency
Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream applications. Nonetheless, as unique data properties have inspired distinct and powerful learning principles, this paper aims to explore their potentials towards mitigating adversarial inputs. In particular, our results reveal the importance of using the temporal dependency in audio data to gain discriminate power against adversarial examples. Tested on the automatic speech recognition (ASR) tasks and three recent audio adversarial attacks, we find that (i) input transformation developed from image adversarial defense provides limited robustness improvement and is subtle to advanced attacks; (ii) temporal dependency can be exploited to gain discriminative power against audio adversarial examples and is resistant to adaptive attacks considered in our experiments. Our results not only show promising means of improving the robustness of ASR systems, but also offer novel insights in exploiting domain-specific data properties to mitigate negative effects of adversarial examples.
 This paper proposed a study on audio adversarial examples and conclude the input transformation-based defenses do not work very well on the audio domain, especially for adaptive attacks. They also point out the importance of temporal dependency in designing defenses which is specific for the audio domain. This observation is very interesting and inspiring as temporal dependency is an important character that should be paid attention to in the field of audio adversarial examples. They also design some adaptive attacks to the defense based on temporal dependency but either fail to attack the system or can be detected by the defense. Based on the results in Table S7, it seems like being aware of the parameter k when designing attacks are very helpful for reducing the AUC score. My question is if the attacker uses the random sample K_A to generate the adversarial examples, then how the performance would be. Another limitation of this work is that the proposed defense can differentiate the adversarial examples to some extent, but the ASR is not able to make a right prediction for adversarial examples. In addition, the writing of Section 4 is not very clear and easy to follow. In all, this paper proposed some interesting findings and point out a very important direction for audio adversarial examples. If the author can improve the writing in experiments and answer the above questions, I would support for the acceptance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Characterizing Audio Adversarial Examples Using Temporal Dependency
Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream applications. Nonetheless, as unique data properties have inspired distinct and powerful learning principles, this paper aims to explore their potentials towards mitigating adversarial inputs. In particular, our results reveal the importance of using the temporal dependency in audio data to gain discriminate power against adversarial examples. Tested on the automatic speech recognition (ASR) tasks and three recent audio adversarial attacks, we find that (i) input transformation developed from image adversarial defense provides limited robustness improvement and is subtle to advanced attacks; (ii) temporal dependency can be exploited to gain discriminative power against audio adversarial examples and is resistant to adaptive attacks considered in our experiments. Our results not only show promising means of improving the robustness of ASR systems, but also offer novel insights in exploiting domain-specific data properties to mitigate negative effects of adversarial examples.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper proposed a study on audio adversarial examples and conclude the input transformation-based defenses do not work very well on the audio domain, especially for adaptive attacks. They also point out the importance of temporal dependency in designing defenses which is specific for the audio domain. This observation is very interesting and inspiring as temporal dependency is an important character that should be paid attention to in the field of audio adversarial examples. They also design some adaptive attacks to the defense based on temporal dependency but either fail to attack the system or can be detected by the defense. Based on the results in Table S7, it seems like being aware of the parameter k when designing attacks are very helpful for reducing the AUC score. My question is if the attacker uses the random sample K_A to generate the adversarial examples, then how the performance would be. Another limitation of this work is that the proposed defense can differentiate the adversarial examples to some extent, but the ASR is not able to make a right prediction for adversarial examples. In addition, the writing of Section 4 is not very clear and easy to follow. In all, this paper proposed some interesting findings and point out a very important direction for audio adversarial examples. If the author can improve the writing in experiments and answer the above questions, I would support for the acceptance.
assistant
 This paper proposed a study on audio adversarial examples and conclude the input transformation-based defenses do not work very well on the audio domain, especially for adaptive attacks. They also point out the importance of temporal dependency in designing defenses which is specific for the audio domain. This observation is very interesting and inspiring as temporal dependency is an important character that should be paid attention to in the field of audio adversarial examples. They also design some adaptive attacks to the defense based on temporal dependency but either fail to attack the system or can be detected by the defense. Based on the results in Table S7, it seems like being aware of the parameter k when designing attacks are very helpful for reducing the AUC score. My question is if the attacker uses the random sample K_A to generate the adversarial examples, then how the performance would be. Another limitation of this work is that the proposed defense can differentiate the adversarial examples to some extent, but the ASR is not able to make a right prediction for adversarial examples. In addition, the writing of Section 4 is not very clear and easy to follow. In all, this paper proposed some interesting findings and point out a very important direction for audio adversarial examples. If the author can improve the writing in experiments and answer the above questions, I would support for the acceptance.