Deep Neural Networks (DNNs) are increasingly applied in the real world in safety critical applications like advanced driver assistance systems. An example for such use case is represented by traffic sign recognition systems. At the same time, it is known that current DNNs can be fooled by adversarial attacks, which raises safety concerns if those attacks can be applied under realistic conditions. In this work we apply different black-box attack methods to generate perturbations that are applied in the physical environment and can be used to fool systems under different environmental conditions. To the best of our knowledge we are the first to combine a general framework for physical attacks with different black-box attack methods and study the impact of the different methods on the success rate of the attack under the same setting. We show that reliable physical adversarial attacks can be performed with different methods and that it is also possible to reduce the perceptibility of the resulting perturbations. The findings highlight the need for viable defenses of a DNN even in the black-box case, but at the same time form the basis for securing a DNN with methods like adversarial training which utilizes adversarial attacks to augment the original training data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study


    Beteiligte:


    Erscheinungsdatum :

    11.07.2021


    Format / Umfang :

    1476351 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Adversarial Attacks on Traffic Sign Recognition: A Survey

    Pavlitska, Svetlana / Lambing, Nico / Zollner, J. Marius | IEEE | 2023


    Adversarial Attacks on Skeleton-Based Sign Language Recognition

    Li, Yufeng / Han, Meng / Yu, Jiahui et al. | TIBKAT | 2023


    Adversarial Attacks on Skeleton-Based Sign Language Recognition

    Li, Yufeng / Han, Meng / Yu, Jiahui et al. | Springer Verlag | 2023


    Convolutional Neural Networks for Traffic Sign Recognition

    Wei, Zhonghua / Gu, Heng / Zhang, Ran et al. | TIBKAT | 2021


    Traffic sign recognition using convolutional neural networks

    Boujemaa, Kaoutar Sefrioui / Bouhoute, Afaf / Boubouh, Karim et al. | IEEE | 2017