Adversarial attacks and defense mechanisms for image classification deep learning models in autonomous driving systems

Advancements in artificial intelligence (AI) and Internet of Things (IoT) technologies have catalyzed the evolution of autonomous driving systems (ADSs), with image classification deep learning (DL) models serving as the cornerstone of their decision-making frameworks. Deep neural networks are employed in highly sophisticated and unforeseeable environments such as advanced industrial automation, autonomous vehicles, and financial forecasting. While these models excel in navigating complex driving scenarios, their susceptibility to adversarial attacks poses significant threats to operational safety and functional integrity. This study delves into the taxonomy of adversarial exploits, dissects cutting-edge defense mechanisms, and examines the delicate equilibrium between adversarial robustness and model generalizability. It accentuates the imperative for adaptive, resource-efficient, and scalable countermeasures capable of dynamic, real-time deployment while advocating for hybrid defense architectures and explainable AI (XAI) to foster system transparency and stakeholder trust. By addressing these systemic vulnerabilities through transferable defense strategies, universal countermeasures, and multidisciplinary collaboration, the study sets the stage for developing fortified ADSs capable of resilient operation in dynamically adversarial ecosystems.