Faults That Fortify: CNN Adversarial Robustness via GPU Undervolting

2026-08-20

AI Summary

本文通过GPU欠压训练CNN，引入随机扰动以提高对抗鲁棒性并降低能耗，解决了CNN易受攻击和训练成本高的问题。

Abstract

Convolutional Neural Networks (CNNs) face a dual challenge: vulnerability to adversarial attacks and prohibitive training cost. Adversarial training is effective but expensive, a burden that grows as learning shifts to the energy-constrained edge. This paper addresses both through GPU undervolting during training. Reducing supply voltage introduces stochastic perturbations that act as implicit regularization, improving robustness while lowering power. We characterize undervolting-induced faults at the bit level, then train LeNet, VGG-6, and MobileNetV3 on MNIST and CIFAR-10 under two training regimes, standard and adversarial, each at nominal and undervolted voltage, and evaluate all models against adversarial attacks. In both regimes, the undervolted model consistently achieves higher adversarial accuracy than its nominal-voltage counterpart, showing that hardware-induced faults strengthen even adversarial training. Because dynamic power scales quadratically with supply voltage, these robustness gains arrive with substantial energy savings. GPU undervolting is therefore a readily deployable hardware-level defense requiring no algorithmic change, and opens a promising direction in which robustness and energy efficiency move together.

Problem

• Convolutional Neural Networks
• adversarial attacks
• training cost
• energy-constrained edge

Innovation

• GPU undervolting
• adversarial robustness
• stochastic perturbations
• energy efficiency