Adversarial Attack and Defense Mechanisms in Deep Learning Systems

Abstract

Deep learning systems have demonstrated remarkable performance across various domains, including autonomous vehicles, medical diagnostics, finance, and cybersecurity. However, their vulnerability to adversarial attacks raises serious concerns regarding their deployment in safety-critical and security-sensitive applications. Adversarial attacks involve subtle perturbations to input data that are often imperceptible to humans but can mislead neural networks into making incorrect or even dangerous predictions. These attacks highlight a fundamental weakness in the robustness of deep learning models and pose a significant challenge to their reliability and trustworthiness.