  As the will to deploy neural networks models on embedded systems grows, and
considering the related memory footprint and energy consumption issues, finding
lighter solutions to store neural networks such as weight quantization and more
efficient inference methods become major research topics. Parallel to that,
adversarial machine learning has risen recently with an impressive and
significant attention, unveiling some critical flaws of machine learning
models, especially neural networks. In particular, perturbed inputs called
adversarial examples have been shown to fool a model into making incorrect
predictions. In this article, we investigate the adversarial robustness of
quantized neural networks under different threat models for a classical
supervised image classification task. We show that quantization does not offer
any robust protection, results in severe form of gradient masking and advance
some hypotheses to explain it. However, we experimentally observe poor
transferability capacities which we explain by quantization value shift
phenomenon and gradient misalignment and explore how these results can be
exploited with an ensemble-based defense.
