  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.
