We thank the reviewer for the constructive feedback. We will make the suggested clarifications and fix the typos. The framework of the paper uses the model to improve the reparameterization directly. Reparameterizing in such an extension is an interesting future direction to explore. We thank the reviewer for the constructive feedback.

We thank all reviewers for their positive reception of our paper and for their constructive feedback. On dual norms and prior work. Thank you for pointing us to the relevant prior work of Demontis et al. and Xu et al. which we apparently missed. We will discuss these connections between our work and the prior work of Demontis et al. and Xu et al. in the Nevertheless, as MNIST is the only vision dataset for which we've been able to train models to high levels of MNIST is clearly not solved from an adversarial robustness perspective. We think this is an interesting open problem for the community to consider.

We thank the reviewers for their thoughtful and constructive feedback. The reviewer notes that the proposed conditional convolution method is novel and shows promising results. The reviewer suggests that results would be more convincing if tested on more network structures. The reviewer suggests we compare our approach with squeeze-and-excite (SE). A1 performance, which includes SE layers.

To answer your question about the baseline, we experimented with two new sample audio generated by the same (Karplus-Strong) algorithm and tested against Alexa. The result is shown in Table.1. The musical audio does not fool Alexa. Thank you again for your constructive feedback! Currently, we are also trying to activate the wake-word using our adversary.

We thank the reviewers for their constructive feedback. We use LSTMs as an "agent" not only to make classification Thanks for pointing out this paper, which will be cited and discussed. We are currently preparing a comparison. Please refer to L221-L225 for more details.

See also response to R2 above.

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