Autonomous vehicles use deep neural networks (DNNs) to build powerful vision perception systems, which provide a theoretical foundation for automated vehicle control. Due to the inherent vulnerability of DNNs, many research works have implemented white-box attacks against automotive vision perception systems in the physical world. However, successful black-box attacks (especially decision-based) in the physical world are rarely mentioned because it is difficult to implement a physical-world adversarial attack without internal knowledge about the vision perception systems. In this paper, we propose PRAD, an end-to-end framework that transfers the existing decision-based black-box adversarial attack algorithms (as the backbone of the framework) targeting the digital domain to the physical world for the first time. Specifically, $T(\cdot)$ is first introduced to simulate the real environment changes, e.g., angle, distance, slight shaking, illumination, etc. Then, and crucially, PRAD bridges the non-differentiable black-box attack and the differentiable $T(\cdot)$ by the $L_{1}$ loss function. We use the traffic sign recognition system in the vision perception system as an object to conduct comprehensive experiments, including different environmental conditions, black-box attack backbones, models, and datasets. The results demonstrate that the generated adversarial examples in the decision-based black-box setting can fool the commercial traffic sign recognition system into outputting designated misclassifications with high success rates and strong robustness in the physical world (average 90% in target attacks and nearly 100% in non-target attacks), which outperforms the state-of-the-art homogeneous attack methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Fooling Decision-Based Black-Box Automotive Vision Perception Systems in Physical World


    Beteiligte:
    Jia, Wei (Autor:in) / Lu, Zhaojun (Autor:in) / Yu, Runze (Autor:in) / Li, Liaoyuan (Autor:in) / Zhang, Haichun (Autor:in) / Liu, Zhenglin (Autor:in) / Qu, Gang (Autor:in)


    Erscheinungsdatum :

    01.07.2024


    Format / Umfang :

    8481366 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    RPAU: Fooling the Eyes of UAVs via Physical Adversarial Patches

    Liu, Taifeng / Yang, Chao / Liu, Xinjing et al. | IEEE | 2024




    Distance Perception in Camera-Based Rear Vision Systems

    Mefford, Mary Lynn / Sivak, Michael / Flannagan, Michael J. | SAE Technical Papers | 2002


    Distance perception in camera-based rear vision systems

    Flannagan,M.J. / Sivak,M. / Mefford,M.L. et al. | Kraftfahrwesen | 2002