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Defense Algorithms Against A Fast Gradient Sign Method Attack
Oğuz Kağan Koçak • Clara Nunes
abstract
Deep learning models (DLMs) are susceptible to malicious manipulations known as adversarial attacks, one of which is Fast Gradient Sign Method (FGSM). This attack generates tiny alterations in input data, leading the target model to misclassify it. This paper aims to provide a comprehensive explanation of how FGSM works, including a detailed analysis of its underlying principles and the mathematical formulation of the attack. Furthermore, we will explore countermeasures designed to mitigate the impact of FGSM attacks. These defenses range from adversarial training and input preprocessing techniques to more advanced methods like defensive distillation and gradient masking.
