Enhancing Robustness of Machine Learning Models Against Adversarial Attacks
Ronak Guliani · 2024
Machine learning models are integral for numerous applications, but they remain increasingly vulnerable to adversarial attacks. These attacks involve subtle manipulation of input data to deceive models, presenting a critical threat to their dependability and security. This thesis addresses the need for strengthening these models against such adversarial attacks. Prior research has primarily focused on identifying specific types of adversarial attacks on a limited range of ML algorithms. However, there is a gap in the evaluation of model resilience across algorithms and in the development of effective defense mechanisms. To bridge this gap, this work adopts a two-phase approach. First, it simulates attacks like the Basic Iterative Method (BIM), DeepFool, and Fast Gradient Sign Method (FGSM)on common ML models trained on MNIST and CIFAR-10, which are used for image processing. This thesis will then discuss defensive strategies, which reduces the sensitivity of the model to input changes to improve model resilience against attacks. The findings are aimed to benefit ML researchers to develop more secure and robust ML systems.