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.

In response to these threats, numerous defense mechanisms have been proposed. Techniques such as adversarial training, defensive distillation, input preprocessing, randomization, and gradient masking aim to improve model robustness. Despite these efforts, many defenses are either computationally expensive, degrade model accuracy on clean data, or are themselves vulnerable to more sophisticated adaptive attacks. Furthermore, the adversarial landscape is continuously evolving, with new attack strategies rapidly emerging.

This paper presents a comprehensive survey of existing adversarial attack methodologies, categorizing them based on their knowledge of the model (white-box vs. black-box) and their objective functions. It also evaluates current defense strategies, discussing their strengths, limitations, and real-world applicability. Finally, we explore ongoing challenges and propose potential future research directions to enhance the security and resilience of deep learning systems against adversarial threats.