Adversarial Attacks and Defenses in Deep Learning Models - Khaja Shahini Begum, Bathina Rajesh Kumar, Gundala Venkata Rama Lakshmi, R S S Raju Battula, Elangovan Muniyandy, Amit Verma, Ajmeera Kiran (International Journal of Intelligent Systems and Applications in Engineering, 2024)
This paper investigates the complex interactions that lead to adversarial weaknesses in deep learning systems. This analyses various adversarial attack strategies, including FGSM and PGD, to evaluate how well they may undermine model fidelity. These results highlight the ongoing cat-and-mouse game between deep-learning security attackers and defenders. Although much progress has been made in increasing model resilience, the lack of a globally defined strategy highlights the necessity for a diversified security policy. This study shows the need for continual innovation and the persistent difficulty of protecting deep learning models against hostile threats