C-LEAD: Contrastive Learning for Enhanced Adversarial Defense

TL;DR

This paper introduces a novel contrastive learning approach to improve the robustness of deep neural networks against adversarial attacks by training with both clean and perturbed images.

Contribution

It proposes a new contrastive learning method specifically designed for adversarial defense, enhancing model robustness by learning more resilient feature representations.

Findings

Significant robustness improvements against adversarial attacks

Contrastive loss enhances feature resilience

Method outperforms existing defenses in experiments

Abstract

Deep neural networks (DNNs) have achieved remarkable success in computer vision tasks such as image classification, segmentation, and object detection. However, they are vulnerable to adversarial attacks, which can cause incorrect predictions with small perturbations in input images. Addressing this issue is crucial for deploying robust deep-learning systems. This paper presents a novel approach that utilizes contrastive learning for adversarial defense, a previously unexplored area. Our method leverages the contrastive loss function to enhance the robustness of classification models by training them with both clean and adversarially perturbed images. By optimizing the model's parameters alongside the perturbations, our approach enables the network to learn robust representations that are less susceptible to adversarial attacks. Experimental results show significant improvements in the model's robustness against various types of adversarial perturbations. This suggests that contrastive loss helps extract more informative and resilient features, contributing to the field of adversarial robustness in deep learning.