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.
@article{arxiv.2001.06099,
title = {Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks},
author = {Farnaz Behnia and Ali Mirzaeian and Mohammad Sabokrou and Sai Manoj and Tinoosh Mohsenin and Khaled N. Khasawneh and Liang Zhao and Houman Homayoun and Avesta Sasan},
journal= {arXiv preprint arXiv:2001.06099},
year = {2020}
}