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    SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications

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    SADA_AAAI.pdf
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    Type
    Article
    Authors
    Hamdi, Abdullah cc
    Müller, Matthias cc
    Ghanem, Bernard cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    VCC Analytics Research Group
    KAUST Grant Number
    RGC/3/3570-01-01
    Date
    2020-04-03
    Permanent link to this record
    http://hdl.handle.net/10754/664563
    
    Metadata
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    Abstract
    One major factor impeding more widespread adoption of deep neural networks (DNNs) is their lack of robustness, which is essential for safety-critical applications such as autonomous driving. This has motivated much recent work on adversarial attacks for DNNs, which mostly focus on pixel-level perturbations void of semantic meaning. In contrast, we present a general framework for adversarial attacks on trained agents, which covers semantic perturbations to the environment of the agent performing the task as well as pixel-level attacks. To do this, we re-frame the adversarial attack problem as learning a distribution of parameters that always fools the agent. In the semantic case, our proposed adversary (denoted as BBGAN) is trained to sample parameters that describe the environment with which the black-box agent interacts, such that the agent performs its dedicated task poorly in this environment. We apply BBGAN on three different tasks, primarily targeting aspects of autonomous navigation: object detection, self-driving, and autonomous UAV racing. On these tasks, BBGAN can generate failure cases that consistently fool a trained agent.
    Citation
    Hamdi, A., Mueller, M., & Ghanem, B. (2020). SADA: Semantic Adversarial Diagnostic Attacks for Autonomous Applications. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 10901–10908. doi:10.1609/aaai.v34i07.6722
    Sponsors
    This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research under Award No. RGC/3/3570-01-01.
    Publisher
    Association for the Advancement of Artificial Intelligence (AAAI)
    Journal
    Proceedings of the AAAI Conference on Artificial Intelligence
    Conference/Event name
    AAAI Conference on Artificial Intelligence
    DOI
    10.1609/aaai.v34i07.6722
    arXiv
    1812.02132
    Additional Links
    https://aaai.org/ojs/index.php/AAAI/article/view/6722
    ae974a485f413a2113503eed53cd6c53
    10.1609/aaai.v34i07.6722
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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