Adversarial Machine Learning: Attacks and Defenses in Deep Learning Models
Abstract
Adversarial attacks pose significant threats to the reliability of deep learning models. This paper surveys the landscape of adversarial machine learning, exploring both attack strategies (e.g., FGSM, PGD) and defense mechanisms (e.g., adversarial training, input transformations). We evaluate the robustness of models under various attack scenarios and propose best practices for securing AI systems.
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