A Comprehensive Survey of Adversarial Attacks and Defense Strategies in Artificial Intelligence Security
TL;DR
This survey provides a comprehensive synthesis of adversarial attacks and defense mechanisms in modern AI security, introducing a structured taxonomy categorizing attacks into evasion, poisoning, and model inversion strategies, evaluated across varying levels of attacker knowledge.
Abstract
The growing deployment of deep learning models in safety-critical domains has exposed the artificial intelligence landscape to a widening array of adversarial threats, where imperceptible input perturbations reliably induce severe misclassifications. This survey provides a comprehensive synthesis of adversarial attacks and defense mechanisms in modern AI security. It introduces a structured taxonomy categorizing attacks into evasion, poisoning, and model inversion strategies, evaluated across varying levels of attacker knowledge. Correspondingly, current defense techniques—including adversarial training, anomaly detection, and gradient masking—are critically reviewed for their resilience against adaptive, real-world adversaries. The survey further examines robustness benchmarking and success rate analysis frameworks, emphasizing the gap between theoretical guarantees and practical deployment. By consolidating recent advances and persistent limitations, this work identifies open research challenges and outlines emerging directions toward provably secure and trustworthy AI systems for real-world applications.