Deep Learning-Enabled Orbital Angular Momentum-Based Information Encryption Transmission
Fu Feng, Junbao Hu, Zefeng Guo, Jia-An Gan, Pengfei Chen, Guangyong Chen, Changjun Min, Xiaocong Yuan, Michael G. Somekh
Abstract
Orbital angular momentum (OAM)-based optical encryption transmission plays an important role in optical communications. However, it remains challenging to encrypt the data with great security and decrypt them with high fidelity while maintaining large-capacity transmission. In this paper, we propose a novel optical encryption transmission technique, which dynamically converts OAM modes into speckle patterns with a randomly shaking diffuser for high-security encryption, and a pretrained convolutional neural network (CNN) is later employed to extract encoded information hidden in the speckle patterns for high-fidelity information decryption. Our experiment demonstrates that the modulated OAM with an interval of a topological charge as small as 0.01 can be recognized by the CNN with an accuracy of 99.83%. To demonstrate its application, a cat image, encoded and encrypted by the designed system, has been successfully decrypted and decoded with a bit error rate of 0.008%. The security mechanism of the technique has also been experimentally verified and discussed. This technique thereby provides a new avenue for OAM-based encrypted optical information transmission.