Deep Learning-Enabled Orbital Angular Momentum-Based Information Encryption Transmission

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.

Intelligent optoelectronic processor for orbital angular momentum spectrum measurement

“…This phenomenon indicates that our optical neural network is actually transforming the wavefront of structured field. In this way, our scheme can naturally get rid of the misleading brought by degenerate intensity patterns, as confronted by earlier works using intensity-based DL recognition algorithms [27,28,30,52].…”

“…In optics, data-driven DL algorithms are becoming pervasive tools to augment performance and to infuse new functionalities in imaging [23], holography [24], ultrafast photonics [25], optical metrology [26], etc. Recently, DL is also introduced to recognize OAM modes [27][28][29][30][31] due to their inherent ability to analyze complex patterns. However, the distinguishment of degenerate intensities patterns carrying different OAM spectra becomes a major challenge.…”

Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map

“…The process of image denoising is considered image restoration [ 11 ]. The authors of [ 12 ] proposed a new cross-image, pixel scrambling-based rotation domain, dual-image encryption technique. In [ 13 ], the authors suggested a dual-image encryption method that incorporates DNA spatiotemporal chaos, deletion, and insertion to improve the security of the encryption process, and scramble real as well as imaginary sections of data produced by every round of encryption [ 14 ].…”

Ultra-Degree-of-Freedom Structured Light for Ultracapacity Information Carriers

“…Here, they deliberately introduced mechanical stress to the multimode fiber to mix the input OAM modes thus to encrypt the message and then a neural network was trained to recover the initial single or superposition modes that could be read out by Bod via a standard alphabet. Similarly, a dynamically shaking diffuser was also exploited for high-security large-capacity encryption transmission technique where OAM beams with an interval of δ l = 0.01 could be successfully decrypted and decoded, as shown in Figure d . Optical speckle patterns naturally complicated the plain-text information in a random manner, which gave rise to the security.…”