C-LEAD: Contrastive Learning for Enhanced Adversarial Defense

A new paper introduces C-LEAD, a method that leverages contrastive learning to enhance the defense of deep neural networks against adversarial attacks. This is significant because while DNNs excel in tasks like image classification and object detection, they are often susceptible to subtle manipulations that can lead to incorrect predictions. By improving the robustness of these systems, C-LEAD could pave the way for more reliable applications in various fields, ensuring that AI technologies remain trustworthy and effective.