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  • Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
Title: Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks
In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity. More specifically, we propose a stacked encoder-convolutional model, in which the input image is first encoded by the encoder module of a denoising auto-encoder, and then the resulting latent representation (without being decoded) is fed to a reduced complexity CNN for image classification. We illustrate that this network not only is more robust to adversarial examples but also has a significantly lower computational complexity when compared to the prior art defenses.  more » « less
Award ID(s):
1718538 2146726
NSF-PAR ID:
10298705
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Date Published:
Journal Name:
2020 21st International Symposium on Quality Electronic Design (ISQED)
Page Range / eLocation ID:
27 to 32
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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