Computer Vision Adversarial Attacks and Defenses: A Comprehensive Review

Abstract

When malicious actors try to trick computer vision models into mislabeling images by creating small, nearly undetected perturbations, the reliability and security of these models are put at risk. gives a comprehensive review of adversarial attack methodologies and how they impact various computer vision applications, such as picture identification, object detection, and face recognition. In order to decrease adversarial vulnerabilities, we examine attack and defense techniques, including defensive distillation, adversarial training, input modification, and ensemble methods. We assess these safeguards against the development of attack tactics by analyzing the trade-offs between computing cost, model correctness, and robustness. critical challenges to secure computer vision models, such as issues with comprehension, adaptability to defenses, and transferability. Continuous research into constructing more robust computer vision systems that can resist hostile manipulation is necessary to increase the safety of AI applications in crucial domains like autonomous driving, healthcare, and security. Our results underscore this importance.