• DOI:10.1109/ISQED48828.2020.9136987
Corpus ID: 210714103

Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks

@article{Behnia2020CodeBridgedC,
  title={Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks},
  author={F. Behnia and Ali Mirzaeian and M. Sabokrou and S. Manoj and T. Mohsenin and Khaled N. Khasawneh and Liang Zhao and Houman Homayoun and Avesta Sasan},
  journal={2020 21st International Symposium on Quality Electronic Design (ISQED)},
  year={2020},
  pages={27-32}
}

F. Behnia, Ali Mirzaeian, +6 authors Avesta Sasan

Published 2020

Computer Science, Mathematics

2020 21st International Symposium on Quality Electronic Design (ISQED)

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… CONTINUE READING

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