Reversible photorefractive attack in continuous-variable quantum key distribution
Authors: Junxuan Liu, Yuchao Liu, Qingquan Peng, Piao Tan, Tao Wang, Peng Huang, Anqi Huang
Abstract
Quantum key distribution (QKD) offers information-theoretic security, but device imperfections introduce implementation vulnerabilities. Experimental evidence of the security consequences of transmitter vulnerabilities in continuous-variable QKD (CV-QKD) remains limited. Here, we experimentally investigate a reversible photorefractive attack in a local-local-oscillator CV-QKD system. Moderate blue-light illumination reduces the modulation variance of a lithium-niobate IQ modulator to a stable value without appreciably increasing estimated excess noise. The variance recovers after illumination is removed. We elucidate the underlying physical mechanisms and develop an attack that exploits recovery to compromise modulation calibration. Receiver measurements confirm that the resulting calibration mismatch leads to overestimated channel transmittance and underestimated excess noise. Key rate calculations based on measured parameters yield a positive estimated rate when the correctly calibrated rate is nonpositive. Recovery can also make the preceding manipulation harder to identify through later inspection. This work provides the first system-level experimental evidence that a reversible photorefractive response can be exploited to compromise modulation calibration in CV-QKD. These findings highlight reversible changes in quantum transmitters as a practical security concern, motivating security analyses and protection strategies that account for both optical perturbations and subsequent recovery.
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In short
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Abstract
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Abstract
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Abstract
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Abstract
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Abstract
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Abstract
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Abstract
Quantum adversarial machine learning is an emerging field that studies the vulnerability of quantum learning systems against adversarial perturbations and develops possible defense strategies. Quantum universal adversarial perturbations are small perturbations, which can make different input samples into adversarial examples that may deceive a given quantum classifier. This is a field that was rarely looked into but worthwhile investigating because universal perturbations might simplify malicious attacks to a large extent, causing unexpected devastation to quantum machine learning models. In this paper, we take a step forward and explore the quantum universal perturbations in the context of …
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