Cloud hosting of quantum machine learning (QML) models exposes them to a
range of vulnerabilities, the most significant of which is the model stealing
attack. In this study, we assess the efficacy of such attacks in the realm of
quantum computing. We conducted comprehensive experiments on various datasets
with multiple QML model architectures. Our findings revealed that model
stealing attacks can produce clone models achieving up to $0.9\times$ and
$0.99\times$ clone test accuracy when trained using Top-$1$ and Top-$k$ labels,
respectively ($k:$ num\_classes). To defend against these attacks, we leverage
the unique properties of current noisy hardware and perturb the victim model
outputs and hinder the attacker's training process. In particular, we propose:
1) hardware variation-induced perturbation (HVIP) and 2) hardware and
architecture variation-induced perturbation (HAVIP). Although noise and
architectural variability can provide up to $\sim16\%$ output obfuscation, our
comprehensive analysis revealed that models cloned under noisy conditions tend
to be resilient, suffering little to no performance degradation due to such
obfuscations. Despite limited success with our defense techniques, this outcome
has led to an important discovery: QML models trained on noisy hardwares are
naturally resistant to perturbation or obfuscation-based defenses or attacks.

云端托管的量子机器学习（QML）模型面临许多漏洞，其中最重要的是模型窃取攻击。本研究评估了这些攻击在量子计算领域的效果，利用多个 QML 模型体系结构在不同数据集上进行了全面的实验。实验结果显示，使用 Top-1 和 Top-k（k: num_classes）标签进行训练的模型窃取攻击可以产生克隆模型，其克隆测试准确率分别达到原模型的 0.9 倍和 0.99 倍。为了防御这些攻击，我们利用当前嘈杂硬件的独特属性扰乱了受害模型的输出，阻碍了攻击者的训练过程。具体来说，我们提出了两种方法：1）硬件变化诱发的扰动（HVIP）和 2）硬件和模型结构变化诱发的扰动（HAVIP）。虽然噪声和架构的可变性可以提供约 16% 的输出混淆，但我们的综合分析表明，在噪声条件下克隆的模型往往是弹性的，由于这种混淆几乎不会导致性能下降。尽管我们的防御技术取得了有限的成功，但这种结果导致了一项重要发现：在噪声硬件上训练的 QML 模型对扰动或混淆性的防御或攻击具有天然的抵抗力。