Revealing spoofing of quantum illumination using entanglement

• URL: http://arxiv.org/abs/2410.08353v1
• Date: Thu, 10 Oct 2024 20:19:11 GMT
• Title: Revealing spoofing of quantum illumination using entanglement
• Authors: Jonathan N. Blakely, Shawn D. Pethel, Kenneth R. Stewart, Kurt Jacobs,
• Abstract summary: We analyze the scenario of a classical radar operator trying to detect the presence of a spoofer. We find that in the absence of noise and loss, direct detection tends to produce spoofs with greater fidelity. Our results suggest that entanglement is a novel resource available to quantum radar for detecting spoofing.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Several quantum radar concepts have been proposed that exploit the entanglement found in two-mode squeezed vacuum states of the electromagnetic field, the most prominent being radar based on quantum illumination. Classical radars are sometimes required to distinguish between true echos of their transmitted signals and signals generated by interferors or spoofers. How vulnerable to spoofing is quantum illumination? We analyze the scenario of a radar operator trying to detect the presence of a classical spoofer employing a measure-and-prepare strategy against a quantum radar. We consider two spoofing strategies - (1) direct detection and number state preparation, and (2) heterodyne detection and coherent state preparation. In each case, the radar operator performs a hypothesis test to decide if received pulses are true returns or spoofs. Since the spoofer can not reproduce the entanglement with modes retained by the radar operator, both approaches to spoofing are to some extent detectable. We quantify the effectiveness of the spoof in terms of the fidelity between the real return and the spoof return, and the probability of error in spoof detection. We find that in the absence of noise and loss, direct detection tends to produce spoofs with greater fidelity, which are therefore harder to detect. Moreover, this advantage survives the introduction of noise and loss into the model. Our results suggest that entanglement is a novel resource available to quantum radar for detecting spoofing.

Related papers

• Quantum enhancement of spoofing detection with squeezed states of light [0.18377902806196764]
  We show that quantum enhancement is independent of the number of photons. We consider encoding squeezed states in the signal and show that the detection probability approaches unity if the spoofer capability is limited to coherent state generation.
  arXiv  Detail & Related papers  (2024-06-20T21:43:56Z)
• On Target Detection by Quantum Radar (Preprint) [1.0878040851637998]
  Noise Radar and Quantum Radar exploit randomness of transmitted signal to enhance radar covertness and to reduce mutual interference. Various Quantum Radar proposals cannot lead to any useful result, especially, but not limited to, the alleged detection of stealth targets.
  arXiv  Detail & Related papers  (2024-02-29T18:58:40Z)
• Revealing spoofing of classical radar using quantum noise [0.0]
  We introduce a model of electromagnetic spoofing that includes effects of practical importance that were neglected in prior theoretical studies. We derive the optimal probability of detecting a spoofer allowed by quantum physics. We show that a high degree of certainty in spoof detection can be reached by Bayesian inference from a sequence of received pulses.
  arXiv  Detail & Related papers  (2023-07-05T21:11:36Z)
• Spatial-Frequency Discriminability for Revealing Adversarial Perturbations [53.279716307171604]
  Vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. Current algorithms typically detect adversarial patterns through discriminative decomposition for natural and adversarial data. We propose a discriminative detector relying on a spatial-frequency Krawtchouk decomposition.
  arXiv  Detail & Related papers  (2023-05-18T10:18:59Z)
• Certified Robustness of Quantum Classifiers against Adversarial Examples through Quantum Noise [68.1992787416233]
  We show that adding quantum random rotation noise can improve robustness in quantum classifiers against adversarial attacks. We derive a certified robustness bound to enable quantum classifiers to defend against adversarial examples.
  arXiv  Detail & Related papers  (2022-11-02T05:17:04Z)
• How can a Radar Mask its Cognition? [19.044614610714856]
  A cognitive radar can em hide its strategy from an adversary that detects cognition. The radar does so by transmitting purposefully designed sub-optimal responses to spoof the adversary's Neyman-Pearson detector. We show that small purposeful deviations from the optimal strategy of the radar confuse the adversary by significant amounts.
  arXiv  Detail & Related papers  (2022-10-20T17:45:55Z)
• Quantum Limits to Classically Spoofing an Electromagnetic Signal [0.0]
  Spoofing an electromagnetic signal involves measuring its properties and preparing a spoof signal that is a close enough copy to fool a receiver. We show that a spoofer optimally employs classical information on the state of the transmitted signal. We show that the quantum limitations on classical spoofing remain significant even in the large mean-photon-number regime.
  arXiv  Detail & Related papers  (2022-01-24T23:11:33Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Optimally Displaced Threshold Detection for Discriminating Binary Coherent States Using Imperfect Devices [50.09039506170243]
  We analytically study the performance of the generalized Kennedy receiver having optimally displaced threshold detection (ODTD) in a realistic situation with noises and imperfect devices. We show that the proposed greedy search algorithm can obtain a lower and smoother error probability than the existing works.
  arXiv  Detail & Related papers  (2020-07-21T21:52:29Z)
• Anomaly Detection-Based Unknown Face Presentation Attack Detection [74.4918294453537]
  Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task.
  arXiv  Detail & Related papers  (2020-07-11T21:20:55Z)
• Using Randomness to decide among Locality, Realism and Ergodicity [91.3755431537592]
  An experiment is proposed to find out, or at least to get an indication about, which one is false. The results of such experiment would be important not only to the foundations of Quantum Mechanics.
  arXiv  Detail & Related papers  (2020-01-06T19:26:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.