  Quantization has emerged as an essential technique for deploying deep neural
networks (DNNs) on devices with limited resources. However, quantized models
exhibit vulnerabilities when exposed to various noises in real-world
applications. Despite the importance of evaluating the impact of quantization
on robustness, existing research on this topic is limited and often disregards
established principles of robustness evaluation, resulting in incomplete and
inconclusive findings. To address this gap, we thoroughly evaluated the
robustness of quantized models against various noises (adversarial attacks,
natural corruptions, and systematic noises) on ImageNet. The comprehensive
evaluation results empirically provide valuable insights into the robustness of
quantized models in various scenarios, for example: (1) quantized models
exhibit higher adversarial robustness than their floating-point counterparts,
but are more vulnerable to natural corruptions and systematic noises; (2) in
general, increasing the quantization bit-width results in a decrease in
adversarial robustness, an increase in natural robustness, and an increase in
systematic robustness; (3) among corruption methods, \textit{impulse noise} and
\textit{glass blur} are the most harmful to quantized models, while
\textit{brightness} has the least impact; (4) among systematic noises, the
\textit{nearest neighbor interpolation} has the highest impact, while bilinear
interpolation, cubic interpolation, and area interpolation are the three least
harmful. Our research contributes to advancing the robust quantization of
models and their deployment in real-world scenarios.
