Adversarial Robustness in Deep Neural Networks: Techniques and Challenges for Secure AI Systems

Abstract

Secure AI systems face substantial hurdles due of Deep Neural Networks' (DNNs) sensitivity to adversarial assaults, notwithstanding DNNs' impressive accomplishment in multiple fields. Essential applications including autonomous driving, healthcare, and financial systems are vulnerable to adversarial assaults, which involve carefully designed perturbations that force algorithms to incorrectly categorize inputs. the reliability and security of DNNs through an exhaustive examination of adversarial robustness methods. We investigate several defense mechanisms, analyzing their advantages, disadvantages, and context-specificity. These mechanisms include adversarial training, gradient masking, defensive distillation, and input modification approaches. We also look at the trade-offs between robustness and model performance, drawing attention to the never-ending battle between improving defenses and creating new attack tactics. This report finds research gaps and suggests future approaches for constructing more resilient and secure DNNs through a comparative examination of current techniques. To guarantee the reliability and security of AI systems in real-world situations, it is essential to enhance adversarial robustness, especially while the AI threat landscape is always changing.