text: The manuscript proposes two objective functions based on the manifold assumption as defense mechanisms against adversarial examples.  The two objective functions are based on assigning low confidence values to points that are near or off the underlying (learned) data manifold while assigning high confidence values to points lying on the data manifold.  In particular, for an adversarial example that is distinguishable from the points on the manifold and assigned a low confidence by the model, is projected back onto the designated manifold such that the model assigns it a high confidence value.  The authors claim that the two objective functions proposed in this manuscript provide such a projection onto the desired manifold and assign high confidence for these adversarial points.  These mechanisms, together with the so-called shell wrapper around the model (a deep learning model in this case) will provide the desired defense mechanism against adversarial examples. The manuscript at the current stage seems to be a preliminary work that is not well matured yet.  The manuscript is overly verbose and the arguments seem to be weak and not fully developed yet.  More importantly, the experiments are very preliminary and there is much more room to deliver more comprehensive and compelling experiments.