Reducing the size of neural network models is a critical step in moving AI
from a cloud-centric to an edge-centric (i.e. on-device) compute paradigm. This
shift from cloud to edge is motivated by a number of factors including reduced
latency, improved security, and higher flexibility of AI algorithms across
several application domains (e.g. transportation, healthcare, defense, etc.).
However, it is currently unclear how model compression techniques may affect
the robustness of AI algorithms against adversarial attacks. This paper
explores the effect of quantization, one of the most common compression
techniques, on the adversarial robustness of neural networks. Specifically, we
investigate and model the accuracy of quantized neural networks on
adversarially-perturbed images. Results indicate that for simple gradient-based
attacks, quantization can either improve or degrade adversarial robustness
depending on the attack strength.

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