Defense Against Adversarial Attacks: Foundations, Strategies, and Future Directions

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Abstract
Adversarial machine learning is an important area of research in computer science, focusing on understanding and mitigating attacks that make use of the vulnerabilities of machine learning models. In such attacks, adversaries aim to exploit these vulnerabilities in order to harm model's utility or violate its privacy or availability. These attacks include evasion, poisoning, exploratory, and explainability. Evasion, poisoning, and explainability attacks aim to harm the models' utility, while exploratory attacks violate the privacy of the models. To reduce the negative impacts of these attacks, a plethora of defense mechanisms have been proposed. In this survey, we review a substantial body of works and propose a comprehensive and novel taxonomy of defense strategies. We divide defense systems into eight main categories including: training-based, architecture-based, uncertainty-based, detection-based, optimization-based, transformation-based, information-theoretic, and hybrid approaches. For each category, we analyze and summarize key techniques and algorithms. Moreover, we investigate the limitations associated with these defenses, such as model generalization, robustness-accuracy trade-off, and adaptability challenges. By highlighting the strengths and weaknesses of existing defenses, this survey aims to enlighten future research towards more robust and efficient adversarial defense mechanisms.

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