부산대학교 도서관

Artificial intelligence technology is leading the innovations in connected and automation vehicles (CAVs). This technology revolution mainly relies on embedded smart devices and high-tech sensors along with deep learning-based modules to provide the data and intellect necessary for automated decisions and responses. Generally, imagery data captured from dash cameras are fed into deep neural network models to identify street signs, traffic lights, and surrounding obj ects to augment steering decisions. Such neural networks are proven to be vulnerable to a wide range of adversarial attacks. Despite the emergence of adversarial manipulations, there has been a dramatic increase in the sophisticated methods of these attacks. One of these methods is the DeepBillBoard attack which uses machine-generated imagery applied to roadside billboards to induce errors to the steering model with the capability to dictate whether this error should cause the vehicle to veer to the left or the right. As the sheer risk of such attacks continues to grow, the safety, security, and reliability concerns grow even more. Such concerns cannot be tolerated given the safety-critical environment where CA V s operate. This paper proposes a novel approach, DeepGuard, to detect, counter, and neutralize DeepBillBoard attacks against CA V s. DeepGuard leverages advanced deep learning techniques to boost its generalization capabilities for detecting adversarial patterns used in DeepBillboard attacks. Experimental evaluation is conducted using existing driving datasets that reflect dynamic real-life scenarios. The evaluation results demonstrate that our solution achieves high detection effectiveness and computational efficiency.