RobustMQ: benchmarking robustness of quantized models

Abstract

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 types of noise 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 types of noise (adversarial attacks, natural corruption, and systematic noise) 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 corruption and systematic noise; 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, impulse noise and glass blur are the most harmful to quantized models, while brightness has the least impact; 4) among different types of systematic noise, the 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.