In recent years, deep neural networks (DNNs) have accomplished impressive success in various applications, including autonomous driving perception tasks. However, current are easily deceived by adversarial attacks. This vulnerability raises significant concerns, particularly safety-critical applications. As a result, research into attacking and defending DNNs has gained much coverage. this work...