Arizona State University

Yuzhen Ding

Baoxin Li

Nupur Thakur

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 convolutional 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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Tempe, AZ, United States of America

Yuzhen Ding