Fault injection attacks on embedded neural network models have been shown as a potent threat. Numerous works studied resilience of models from various points of view. As of now, there is no comprehensive study that would evaluate the influence of number representations used for model parameters against electromagnetic fault injection (EMFI) attacks. In this paper, we investigate how four different number representations influence the success of an EMFI attack on embedded neural network models. We chose two common floating-point representations (32-bit, and 16-bit), and two integer representations (8-bit, and 4-bit). We deployed four common image classifiers, ResNet-18, ResNet-34, ResNet-50, and VGG-11, on an embedded memory chip, and utilized a low-cost EMFI platform to trigger faults. Our results show that while floating-point representations exhibit almost a complete degradation in accuracy (Top-1 and Top-5) after a single fault injection, integer representations offer better resistance overall. Especially, when considering the the 8-bit representation on a relatively large network (VGG-11), the Top-1 accuracies stay at around 70% and the Top-5 at around 90%.

翻译：针对嵌入式神经网络模型的故障注入攻击已被证明是一种强有力的威胁。众多研究从不同角度探讨了模型的抗扰性。迄今为止，尚无全面研究评估模型参数所用数值表示形式对电磁故障注入攻击的影响。本文研究了四种不同的数值表示形式如何影响对嵌入式神经网络模型的EMFI攻击成功率。我们选择了两种常见的浮点表示形式，以及两种整数表示形式。我们将四种常见的图像分类器部署于嵌入式存储芯片上，并利用低成本EMFI平台触发故障。实验结果表明：浮点表示形式在单次故障注入后几乎表现出精度完全退化，而整数表示形式总体上具有更好的抗扰性。特别是在相对较大的网络上使用8位表示形式时，其Top-1精度保持在70%左右，Top-5精度保持在90%左右。