AI RESEARCH
C-LEAD: Contrastive Learning for Enhanced Adversarial Defense
arXiv CS.CV
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ArXi:2510.27249v2 Announce Type: replace 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.