Adversarial Attacks in Computer Vision: Challenges and Defense Strategies

Adversarial attacks have emerged as one of the most critical vulnerabilities in modern computer vision systems powered by deep learning. Despite their remarkable accuracy and generalization capabilities, convolutional neural networks (CNNs), vision transformers (ViTs), and other deep models remain highly susceptible to imperceptible perturbations crafted by adversaries. These perturbations can mislead models into producing incorrect outputs with high confidence, leading to severe consequences in domains such as autonomous driving, biometric authentication, medical imaging, and surveillance. This paper provides an extensive examination of adversarial attacks in computer vision, categorizing them into white-box, black-box, targeted, and untargeted variants. We explore well-known attack techniques such as the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), Carlini & Wagner (C&W), and transferability-based black-box strategies. Furthermore, we review state-of-the-art defense mechanisms, including adversarial training, input preprocessing, gradient masking, certified defenses, and robust optimization. A statistical analysis is provided to evaluate the performance degradation of vision models under adversarial conditions and the improvement achieved through defense strategies. Our methodology integrates systematic literature review, empirical evaluation, and comparative simulation on benchmark datasets such as MNIST, CIFAR-10, and ImageNet. Results highlight that adversarial training remains the most effective defense but comes at the cost of computational overhead and reduced clean accuracy. The paper concludes by identifying gaps in current defense research and outlining future directions, including adaptive hybrid defenses, explainable adversarial robustness, and biologically inspired vision architectures. The study contributes a comprehensive understanding of adversarial machine learning in computer vision and provides a roadmap for building more secure and trustworthy AI systems.