QUASAR：一种用于SAR卫星物理层认证的量子-经典神经网络
QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authentication
- University of Pisa（比萨大学）
- King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出首个量子-经典混合架构QUASAR，融合CNN与VQC实现X波段SAR卫星物理层认证，数据效率更高、准确率更优，在三类对抗场景下表现出良好的欺骗传输拒绝能力。
AI中文摘要：
X波段SAR卫星（8-12 GHz）在灾害响应、环境监测和军事情报领域发挥着关键作用，但它们缺乏强大的物理层认证（PLA），这是一种与加密解决方案正交的安全层。现有的PLA系统通常基于射频指纹，往往局限于6 GHz以下的频率，且依赖经典深度学习。然而，这种方法对区分卫星硬件的IQ相位非线性拟合不足。本文提出了QUASAR，据我们所知，这是首个将CNN频谱图编码器与变分量子电路（VQC）融合的量子-经典混合架构，用于对X波段SAR信号进行PLA。我们的解决方案具有两个显著特征：（i）它比经典机器学习的数据效率显著更高，仅需10%的训练数据即可达到经典基线的准确率——数据采集是PLA中最耗时的阶段；（ii）在相同数据预算下，它的分类准确率优于这些基线。具体而言，我们在三种对抗场景下测试了我们的解决方案：重放、定制IQ注入和星载欺骗。QUASAR分别在89.7%、94.1%和81.3%的尝试中拒绝了欺骗传输，成为首个用于卫星星座的量子增强物理层分类器。完整详细的框架和支持结果除了本身具有研究价值外，还为物理层认证开辟了一条新的研究途径。
英文摘要：
X-band SAR satellites (8-12 GHz) play a critical role in disaster response, environmental monitoring, and military intelligence. Yet, they lack robust physical-layer authentication (PLA), a security layer orthogonal to cryptographic solutions. Existing PLA systems, typically based on radio-frequency fingerprinting, are often limited to sub-6 GHz frequencies and rely on classical deep learning. However, this approach underfits the IQ phase nonlinearities that distinguish satellite hardware. In this paper, we present QUASAR, to the best of our knowledge the first quantum-classical hybrid architecture that fuses a CNN spectrogram encoder with a variational quantum circuit (VQC) to provide PLA to X-band SAR signals. Our solution enjoys two distinctive features: (i) it is markedly more data-efficient than classical machine learning, requiring only 10% of the training data to match the accuracy of classical baselines -- data collection being notoriously the most time-consuming phase of PLA; and, (ii) at an equal data budget, it improves classification accuracy over those baselines. In detail, we test our solution under three adversarial scenarios: replay, crafted-IQ injection, and space-borne spoofing. QUASAR rejects spoofed transmissions in 89.7%, 94.1%, and 81.3% of attempts, respectively, establishing the first quantum-enhanced physical-layer classifier for satellite constellations. The fully detailed framework and the supporting results, other than being interesting on their own, show a novel research avenue for physical-layer authentication.
