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Visual image encryption based on compressed sensing and Cycle-GAN

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Abstract

At present, most image encryption schemes directly change plaintext images into ciphertext images without visual significance, and such ciphertext images can be detected by hackers during transmission, and therefore subject to various attacks. To protect the content security and visual safety of images, a learning visual image encryption scheme based on compressed sensing (CS) and cycle generative adversarial network is proposed. First, the secret image is sparse by discrete wavelet transform and compressed by CS. Secondly, the compressed image is permuted and diffused by an improved Henon map to obtain the ciphertext image. Finally, the images are migrated from the ciphertext domain to the plaintext domain by generating an adversarial network to obtain visually meaningful images. We constrain and guide the image generation process by introducing a feature loss function to guarantee the quality of the reconstructed images. Experimental results and security analysis show that the image encryption scheme has sufficient key space, strong key sensitivity, and high reconstruction quality.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This project is supported in part by the National Natural Science Foundation of China: 62262062, the major programs incubation plan of **zang Minzu University: 22MDZ03, and the Research Team Project for **zang-related Network Information Content and Data Security (No. 324042000709).

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Authors and Affiliations

  1. School of Information Engineering, **zang Minzu University, **anyang, 712082, Shaanxi, China

    Zhaoyang Liu & Ru Xue

  2. Key Laboratory of Optical Information Processing and Visualization Technology of Tibet Autonomous Region, **anyang, 712082, Shaanxi, China

    Zhaoyang Liu & Ru Xue

  3. **zang Cyberspace Governance Research Center, **anyang, China

    Zhaoyang Liu & Ru Xue

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  1. Zhaoyang Liu
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  2. Ru Xue
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Correspondence to Ru Xue.

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Liu, Z., Xue, R. Visual image encryption based on compressed sensing and Cycle-GAN. Vis Comput (2023). https://doi.org/10.1007/s00371-023-03140-1

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