Optical Compressive Encryption via Deep Learning

• Main Authors: Yi Qin, Yuhong Wan, Shujia Wan, Chao Liu, Wei Liu
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Photonics Journal
• Subjects: Optical security; ciphertext compression; deep learning
• Online Access: https://ieeexplore.ieee.org/document/9478270/

The compression of the ciphertext of a cryptosystem is desirable considering the dramatic increase in secure data transfer via Internet. In this paper, we propose a simple and universal scheme to compress and decompress the ciphertext of an optical cryptosystem by the aid of deep learning (DL). For compression, the ciphertext is first resized to a relatively small dimension by bilinear interpolation and thereafter condensed by the JPEG2000 standard. For decompression, a well-trained deep neural network (DNN) can be employed to perfectly recover the original ciphertext, in spite of the severe information loss suffered by the compressed file. In contrast with JPEG2000 and JPEG, our proposal can achieve a far smaller size of the compressed file (SCF) while offering comparable decompression quality. In addition, the SCF can be further reduced by compromising the quality of the recovered plaintext. It is also shown that the compression procedure can provide an additional security level, and this may offer new insight into the compressive encryption in optical cryptosystems. Both simulation and experimental results are presented to demonstrate the proposal.