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arXiv · 2108.09413

Integer-arithmetic-only Certified Robustness for Quantized Neural Networks

Haowen Lin·Jian Lou·Li Xiong·Cyrus Shahabi
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Abstract

Adversarial data examples have drawn significant attention from the machine learning and security communities. A line of work on tackling adversarial examples is certified robustness via randomized smoothing that can provide a theoretical robustness guarantee. However, such a mechanism usually uses floating-point arithmetic for calculations in inference and requires large memory footprints and daunting computational costs. These defensive models cannot run efficiently on edge devices nor be deployed on integer-only logical units such as Turing Tensor Cores or integer-only ARM processors. To overcome these challenges, we propose an integer randomized smoothing approach with quantization to convert any classifier into a new smoothed classifier, which uses integer-only arithmetic for certified robustness against adversarial perturbations. We prove a tight robustness guarantee under L2-norm for the proposed approach. We show our approach can obtain a comparable accuracy and 4x~5x speedup over floating-point arithmetic certified robust methods on general-purpose CPUs and mobile devices on two distinct datasets (CIFAR-10 and Caltech-101).

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Haowen Lin, Jian Lou, Li Xiong, Cyrus Shahabi. 2021-08-21. Integer-arithmetic-only Certified Robustness for Quantized Neural Networks. https://arxiv.org/abs/2108.09413

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