Reviews: Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks

This paper proposes a computationally efficient calculation technique that lower-bounds the size of adversarial perturbations that can deceive networks, and an effective training procedure, which robustifies networks and significantly improves the provably guarded areas around data points. The contribution of this paper is proposing an intuitive way to measure the slope to calculate the upper-bounds of gradient and provide a widely available and highly scalable method that ensures large guarded areas for a wide range of network structures. There are certain contribution and originality in the literature. Here I am concerned with the following two questions: 1. This paper defines a guarded area for a network F and a data point X as a hypersphere with a radius c.