Dissecting Adversarial Attacks: A Comparative Analysis of Adversarial Perturbation Effects on Pre-Trained Deep Learning Models

Abstract

It is well known that the majority of neural networks widely employed today are extremely susceptible to adversarial perturbations which causes the misclassification of the output. This, in turn, can cause severe security concerns. In this paper, we meticulously evaluate the robustness of prominent pre-trained deep learning models against images that are modified with the Fast Gradient Sign Method (FGSM) attack. For this purpose, we have selected the following models: InceptionV3, InceptionResNetV2, ResNet152V2, Xception, DenseNet121, and MobileNetV2. All these models are pre-trained on ImageNet, and hence, we use our custom 10- animals test dataset to produce clean as well as misclassified output. Rather than focusing solely on prediction accuracy, our study uniquely quantifies the perturbation required to alter output labels, shedding light on the models' susceptibility to misclassification. The outcomes underscore varying vulnerabilities among the models to FGSM attacks, providing nuanced insights crucial for fortifying neural networks against adversarial threats. Key Words: Adversarial Perturbations, Deep Learning, ImageNet, FGSM Attack, Neural Networks, Pre-trained Models