SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters
Abstract
In this paper, we introduce a novel technique based on the Secure Selective Convolutional (SSC) techniques in the training loop that increases the robustness of a given DNN by allowing it to learn the data distribution based on the important edges in the input image. We validate our technique on Convolutional DNNs against the state-of-the-art attacks from the open-source Cleverhans library using the MNIST, the CIFAR-10, and the CIFAR-100...
Paper Details
Title
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters
Published Date
Nov 4, 2018
Journal
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