Paper:
We provide recovery guarantees for compressible signals that have been corrupted with noise and extend the framework introduced in Bafna et al. (2018) to defend neural networks against 0 , 2 , and ∞ -norm attacks. In the case of 0 -norm noise, we provide recovery guarantees for Iterative Hard Thresholding (IHT) and Basis Pursuit (BP). For 2 -norm bounded noise, we provide recovery guarantees for BP, and for the case of ∞ -norm bounded noise, we provide recovery guarantees for a modified version of Dantzig Selector (DS). These guarantees theoretically bolster the defense framework introduced in Bafna et al. (2018) for defending neural networks against adversarial inputs. Finally, we experimentally demonstrate the effectiveness of this defense framework against an array of 0 , 2 and ∞ -norm attacks.

Review:
This paper extends the compressive recovery defense framework introduced by Bafna et al. (2018), which is mainly against l_0 attacks, to l_2 and l_∞ attacks. They provide guarantees for some recovery algorithms in the case of different kinds of norm bounded noises. The difference between their work and the previous work is clearly clarified. 
Overall, this paper is a follow-up work towards Bafna et al. (2018) but with better theoretical guarantees and ample experiment results to support their robustness against various popular attacks. Given their contribution and inspiration for future work, I think this paper could be accepted to the 2020 ICLR conference. 
In Section 3.2 Recovery Algorithms, the author clearly states three algorithms including IHT, BP, DS, and their modification from the standard ones, but fails to compare the differences between these algorithms. It is not clear about the author’s motivations to proposes these different recovery algorithms and whether their performance varies from each other also remains unknown. Maybe some analysis about their disadvantages and advantages in varied conditions of attacks could be necessary. 
In the Section 3.4 Comparison to Related Work, the author mentions many works aiming at defending against adversarial inputs. However, Bafna et al. (2018) is the only work here that has something to do with compressive sensing. I think maybe the paper should involve some related work here regarding theory of compressive coding besides Bafna et al. (2018). And how they are combined to the defense against adversarial inputs. It would help the readers to have better understanding towards the novelty and breakthroughs in this aspect. 
For the experiments, it would be better to have the comparisons between the proposed algorithm and related methods. Also, the proposed IHT and DS are modified versions. What are the differences in experiments? 
Minor comments:
- Page 2: the line above the equation 1 ‘meaning that x_t (k)…..’, it could be (x_t ) ̂(k)
- Page 6: in the explanation of figure 2, the adversarial inputs are second column and fifth column not fourth column.