Robust Scene-Oriented Adversarial Patch Against Autonomous Driving Perception Systems in Dynamic Industrial Environments

Deep-neural-network-based autonomous driving perception systems in the Industrial Internet of Things remain vulnerable to adversarial attacks despite their critical role in Industry 4.0. While numerous studies have investigated adversarial attacks on individual perception tasks such as monocular depth estimation or object detection in static situations, real-world complex industrial scenarios introduce two understudied challenges: 1) dynamic scenarios with moving objects and changing viewpoints and 2) simultaneous attacks across multiple perception tasks. In this article, we make three key contributions to address these challenges. First, we introduce a systematic study of environment-aware adversarial attacks in dynamic scenarios by introducing three adversarial attack tasks: object away attack, object close attack, and object creation attack. Second, we propose a novel dynamic scene-oriented adversarial road patch generation framework that accounts for real-world environmental variations. Third, we develop a comprehensive technical framework featuring: improved adversarial loss functions, a dynamic optimization architecture, and an integrated approach combining expectation over transformation with advanced physical augmentation optimization. Extensive experimental results demonstrate the robustness of our proposed method in digital, simulation, and real-world physical domains, as well as under different weather conditions.