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In this paper, we propose Code-Bridged Classifier (CBC), a framework formaking a Convolutional Neural Network (CNNs) robust against adversarial attackswithout increasing or even by decreasing the overall models' computationalcomplexity. More specifically, we propose a stacked encoder-convolutionalmodel, in which the input image is first encoded by the encoder module of adenoising auto-encoder, and then the resulting latent representation (withoutbeing decoded) is fed to a reduced complexity CNN for image classification. Weillustrate that this network not only is more robust to adversarial examplesbut also has a significantly lower computational complexity when compared tothe prior art defenses.

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