Efficient Algorithmic Models for Improved Adversarial Attacks and Defenses in Deep Learning Based Image Recognition Models

Abstract: This paper delves into the critical concern of ensuring the safety as well as the reliability of models for deep learning amidst the escalating landscape of adversarial attacks. Techniques like FGSM, DeepFool and PGD pose substantial threats by manipulating input data, leading to erroneous outcomes within machine learning systems. Addressing this challenge head-on, our study introduces an innovative model explicitly engineered to counteract these adversarial threats. Our model specifically focuses on combating the notorious attack algorithms of FGSM, DeepFool and PGD by implementing robust defense mechanisms such as Adversarial Training and GANs. Through meticulous evaluations spanning diverse datasets, including CIFAR and MNIST, our model's efficacy in defending against these sophisticated attacks was rigorously assessed. The empirical results underscore the resilience of our model, showcasing its effectiveness in fortifying deep learning frameworks against hostile intrusions across varied datasets. Our research contributes crucial insights and formidable defense mechanisms, augmenting the security, trust, and reliability of these systems, even amidst the complex challenges posed by adversarial manipulations.