Multiple-image encryption and hiding with an optical diffractive neural network

TL;DR

This paper introduces a multiplexed optical diffractive neural network capable of processing multiple input images simultaneously, enabling efficient image encryption and hiding with independent output regions, optimized via wavefront matching.

Contribution

It presents a novel multiplexed optical DNN architecture that processes multiple images concurrently, enhancing optical encryption and watermarking capabilities.

Findings

Multiple images processed simultaneously with non-overlapping outputs

Effective optimization of phase masks using wavefront matching

Employs orthogonality for multiplexed DNN design

Abstract

A cascaded phase-only mask architecture (or an optical diffractive neural network) can be employed for different optical information processing tasks such as pattern recognition, orbital angular momentum (OAM) mode conversion, image salience detection and image encryption. However, for optical encryption and watermarking applications, such a system usually cannot process multiple pairs of input images and output images simultaneously. In our proposed scheme, multiple input images can be simultaneously fed to an optical diffractive neural network (DNN) system and each corresponding output image will be displayed in a non-overlap sub-region in the output imaging plane. Each input image undergoes a different optical transform in an independent channel within the same system. The multiple cascaded phase masks in the system can be effectively optimized by a wavefront matching algorithm. Similar to recent optical pattern recognition and mode conversion works, the orthogonality property is employed to design a multiplexed DNN.