Boosted by the evolution of machine learning technology, large amount of data and advanced computing system, neural networks have achieved state-of-the-art performance that even exceeds human capability in many applications [1] [2] . However, adversarial attacks targeting neural networks have demonstrated detrimental impact in autonomous driving [3] . The adversarial attacks are capable of arbitrarily manipulating the neural network classification results with different input data which is non-perceivable to human.


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    Titel :

    An Autoencoder Based Approach to Defend Against Adversarial Attacks for Autonomous Vehicles


    Beteiligte:
    Gan, Houchao (Autor:in) / Liu, Chen (Autor:in)


    Erscheinungsdatum :

    01.02.2020


    Format / Umfang :

    170964 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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