Year
Month
(Peer-Reviewed) Multi-dimensional multiplexing optical secret sharing framework with cascaded liquid crystal holograms
Keyao Li 李珂瑶 ¹ ², Yiming Wang 王一鸣 ³, Dapu Pi 皮大普 ¹, Baoli Li 李保莉 ¹, Haitao Luan 栾海涛 ¹, Xinyuan Fang 方心远 ¹, Peng Chen 陈鹏 ³, Yanqing Lu 陆延青 ³, Min Gu 顾敏 ¹
¹ Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
中国 上海 上海理工大学光子芯片研究院
² Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
中国 上海 上海理工大学光电信息与计算机工程学院 人工智能纳米光子学中心
³ National Laboratory of Solid State Microstructures, and College of Engineering and Applied Sciences, Nanjing University, Nanjing 210093, China
中国 南京 南京大学现代工程与应用科学学院 固体微结构物理国家重点实验室
Opto-Electronic Advances, 2024-01-25
Abstract

Secret sharing is a promising technology for information encryption by splitting the secret information into different shares. However, the traditional scheme suffers from information leakage in decryption process since the amount of available information channels is limited.

Herein, we propose and demonstrate an optical secret sharing framework based on the multi-dimensional multiplexing liquid crystal (LC) holograms. The LC holograms are used as spatially separated shares to carry secret images. The polarization of the incident light and the distance between different shares are served as secret keys, which can significantly improve the information security and capacity.

Besides, the decryption condition is also restricted by the applied external voltage due to the variant diffraction efficiency, which further increases the information security. In implementation, an artificial neural network (ANN) model is developed to carefully design the phase distribution of each LC hologram.

With the advantage of high security, high capacity and simple configuration, our optical secret sharing framework has great potentials in optical encryption and dynamic holographic display.
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  • Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates                                Physics-informed deep learning for fringe pattern analysis
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