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    SYSTEMS, METHODS, AND APPARATUSES FOR INTEGRATING A DEFENSE MECHANISM INTO DEEP-LEARNING-BASED SYSTEMS TO DEFEND AGAINST ADVERSARIAL ATTACKS
    2.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR INTEGRATING A DEFENSE MECHANISM INTO DEEP-LEARNING-BASED SYSTEMS TO DEFEND AGAINST ADVERSARIAL ATTACKS 有权

    公开(公告)号：US20230018948A1

    公开(公告)日：2023-01-19

    申请号：US17730051

    申请日：2022-04-26

    Applicant: Arizona Board of Regents on behalf of Arizona State University

    Inventor： Yuzhen Ding ,  Nupur Thakur ,  Baoxin Li

    IPC: G06F21/55 ,  G06N20/10 ,  G06N3/063 ,  G06N3/04

    Abstract: Described herein are means for integrating a defense mechanism into deep-learning-based systems to defend against adversarial attacks. For instance, an exemplary system is specially configured for adding a convolutinal defense layer to a neural network containing orthogonal kernels. Such a system generates the convolutional defense layer based on generating a set of learned kernals to increase diversity of network architecture, in which generating the set of learned kernals includes feeding an output of the convolutional defense layer into the neural network, further in which generating the convolutional defense layer includes selecting one or more orthogonal kernals, duplicating as needed and arranging them in a particular order. Such an embodiment further includes training the neural network with the added convolutional defense layer based on the increased diversity of network architecture; and defending against adverse attacks via constraining the effect of adversarial data generated by the adversarial attacks.

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