  Adversarial examples have emerged as a significant threat to machine learning
algorithms, especially to the convolutional neural networks (CNNs). In this
paper, we propose two quantization-based defense mechanisms, Constant
Quantization (CQ) and Trainable Quantization (TQ), to increase the robustness
of CNNs against adversarial examples. CQ quantizes input pixel intensities
based on a "fixed" number of quantization levels, while in TQ, the quantization
levels are "iteratively learned during the training phase", thereby providing a
stronger defense mechanism. We apply the proposed techniques on undefended CNNs
against different state-of-the-art adversarial attacks from the open-source
\textit{Cleverhans} library. The experimental results demonstrate 50%-96% and
10%-50% increase in the classification accuracy of the perturbed images
generated from the MNIST and the CIFAR-10 datasets, respectively, on commonly
used CNN (Conv2D(64, 8x8) - Conv2D(128, 6x6) - Conv2D(128, 5x5) - Dense(10) -
Softmax()) available in \textit{Cleverhans} library.
