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A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference
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使用注意力插入物理驱动的单像素成像的光学加密.

Wen-Kai Yu1, Shuo-Fei Wang1, Ke-Qian Shang1

  • 1Center for Quantum Technology Research, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement of Ministry of Education, School of Physics, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种通过注意力插入的物理驱动的神经网络,用于使用单像素成像 (SPI) 的光学加密. 这种新的方法消除了预训练,使图像加密更快,更适应,更安全.

关键词:
注意力模块的注意力模块.图像重建 图像重建通过光学加密进行加密.由物理驱动的神经网络.一个像素的成像.

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科学领域:

  • 光学和光子学 在光学和光子学.
  • 计算机科学 计算机科学
  • 密码学 密码学 密码学 密码学

背景情况:

  • 深度学习具有先进的光学加密,但需要广泛的培训和再培训.
  • 当前的方法在动态场景的适应性和计算效率方面存在局限性.

研究的目的:

  • 开发一种新的单像素成像 (SPI) 加密方案,克服传统深度学习方法的局限性.
  • 引入一个以注意力为导向的物理驱动的神经网络,以实现高效和安全的光学加密.

主要方法:

  • 注意模块将图像数据和加密密钥加密为1D加密文本信号.
  • 一个由物理驱动的神经网络解码了加密文本以实现高保真解密.
  • 该方案整合了空间调制自由,并消除了网络预培训.

主要成果:

  • 通过模拟和实验证明了可行性.
  • 在没有事先的网络培训的情况下实现了高保真解密.
  • 展示了窃听的抵抗力,以加强安全.

结论:

  • 拟议的方案为基于SPI的光学加密提供了一个智能和可适应的解决方案.
  • 这种方法可以减少计算开销和培训时间.
  • 它为更先进,深度学习集成的光学安全系统铺平了道路.