Experimental optical encryption based on random mask encoding and deep learning
Xiaogang Wang, Haoyu Wei, Minxu Jin, Bijun Xu, Junlang Chen
Abstract
We present an experimental scheme for optical encryption using random mask encoding and deep learning technique. A phase image is encrypted into a speckle pattern by a random amplitude modulation in the optical transmission. Before decryption processing, a neural network model is used to learn the mapping relationship between the pure-phase object and the speckle image rather than characterizing the filter film used in the scheme explicitly or parametrically. The random binary mask is made by a polyethylene terephthalate film and 2500 object-speckle pairs are used for training. The experimental results demonstrate that the proposed scheme based on deep learning could be successfully used as a random binary mask encrypted image processor, which can quickly output the primary image with high quality from the cyphertext.