Paper:
We present a Deep Neural Network with Spike Assisted Feature Extraction (SAFE-DNN) to improve robustness of classification under stochastic perturbation of inputs. The proposed network augments a DNN with unsupervised learning of lowlevel features using spiking neuron network (SNN) with Spike-Time-DependentPlasticity (STDP). The complete network learns to ignore local perturbation while performing global feature detection and classification. The experimental results on CIFAR-10 and ImageNet subset demonstrate improved noise robustness for multiple DNN architectures without sacrificing accuracy on clean images.

Review:
The paper develops a method to augment deep neural networks with Spike-Time-Dependent-Plasticity (STDP) aiming at improving noise robustness of the network learned features. In the hybrid network, learned feature is the concatenation of traditionally supervised-ly learned features and those from an auxiliary module trained locally and unsupervised-ly by STDP. The new network demonstrates improved noise robustness via improved classification accuracy on Cifar10 and ImageNet subset when input data have noise, on different network architectures.
The paper, however, fails to address the many works in the literature about adversarial perturbations ('attack') and adversarial training ('defense'), starting by (Szegedy et al., 2013). The different types of attacks affect the efficiency of defense due to the game-theoretical nature of the adversarial perturbation problem. If the attack is blind to the classification model, e.g., Gaussian attack by adding Gaussian noise, then image restoration techniques like denoising could provide an effective 'defense'. Thus model-specific attacks are of more application interest than model-blind ones. The current manuscript did not address the specific noise type being used to perturb the image. It is unlikely that the local learning techniques proposed in the paper can work on many kinds of perturbations especially the 'attacks' which is model specific. 
The proposed methodology is a feature concatenation of local (low-level) features of image data and deep features. Given the current state of the manuscript, the level of methodological novelty and the scope of input perturbations that can be made robust against both appear to be limited.
References:
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199, 2013.