QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authentication
2026-08-20 • Artificial Intelligence
Artificial IntelligenceCryptography and Security
AI summaryⓘ
The authors developed a new security method called QUASAR to better identify signals from X-band SAR satellites, which are important for things like disaster response and military use. Unlike older methods, their system mixes classical machine learning with quantum computing, making it much more efficient and accurate even with less data. They tested QUASAR against common fake signal attacks and found it could correctly reject most spoofed transmissions. This work introduces a new way to secure satellite communication at the physical layer using quantum technologies.
X-band SAR satellitesphysical-layer authenticationquantum-classical hybridconvolutional neural networkvariational quantum circuitradio-frequency fingerprintingspoofing attacksmachine learningsignal classificationdata efficiency
Authors
Vincenzo Sammartino, Nathanael Denis, Roberto Di Pietro
Abstract
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.