Artificial key fingerprints for continuous-variable quantum key distribution

With rapid progress and significant successes in quantum hacking attacks, machine learning is being applied in continuous-variable quantum key distribution (CVQKD) systems. However, most machine-learning networks are shown to be vulnerable to adversarial examples. Thus, we propose a defense scheme with artificial key fingerprints for CVQKD systems. We first embed artificial key fingerprints into quadrature values, then exploit changes of fingerprints in the communication process to probe whether the samples have been manipulated. The paper verifies the effectiveness of our proposal based on four groups of samples that consist of normal measurement values and abnormal measurement values under four attacks. Simulation results show that the model can monitor sample changes in real time with high precision, and our scheme stays robust against adversarial examples attack. This proposal provides a different idea to address the potential vulnerability between machine-learning models used in CVQKD systems and their possible attacked training and test data by adversarial examples.