Advanced Adversarial Attacks in Deep Learning: Techniques, Challenges, and Countermeasures

Abstract

Adversarial attacks pose significant threats to the robustness and reliability of machine learning models. These attacks, which involve subtly perturbing input data to mislead models, can compromise the performance of even the most advanced systems. This paper explores novel adversarial attack techniques, examining their methodologies, effectiveness, and implications. We review the evolution of adversarial attacks, introduce innovative approaches, and discuss potential defenses. By understanding these emerging threats, we aim to bolster the resilience of machine learning models in increasingly adversarial environments.