Revisiting Min-Max Optimization Problem in Adversarial Training

TL;DR

This paper proposes a new approach to improve the robustness of deep neural networks against adversarial attacks by reformulating the min-max optimization problem, providing stronger security guarantees.

Contribution

It introduces a novel reformulation of the adversarial training optimization problem to enhance model robustness and security guarantees.

Findings

Significant resistance to adversarial attacks.

Concrete security guarantees demonstrated.

Advances towards fully robust deep learning models.

Abstract

The rise of computer vision applications in the real world puts the security of the deep neural networks at risk. Recent works demonstrate that convolutional neural networks are susceptible to adversarial examples - where the input images look similar to the natural images but are classified incorrectly by the model. To provide a rebuttal to this problem, we propose a new method to build robust deep neural networks against adversarial attacks by reformulating the saddle point optimization problem in \cite{madry2017towards}. Our proposed method offers significant resistance and a concrete security guarantee against multiple adversaries. The goal of this paper is to act as a stepping stone for a new variation of deep learning models which would lead towards fully robust deep learning models.