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Color image encryption based on lite dense-ResNet and bit-XOR diffusion

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Abstract

Images contain a wealth of information and are frequently targeted by malicious attackers when transmitted over public networks. Fortunately, image encryption prevents confidential information from being acquired by illegal attackers. Deep learning-based image encryption is a relatively new research area, but recently proposed methods have not achieved satisfactory levels of generalization, security, and efficiency. To address these limitations, we employ a lite dense residual network (Dense-ResNet) to rearrange image pixels, thereby reducing the computation amounts. In addition, we design a weight-adjustable loss function model, which combines the encryption loss function, decryption loss function, and total variational loss function. And then we adopt bit-XOR diffusion to further encrypt the intermedia ciphertext image obtained by the encryption network. We trained and tested encryption and decryption neural networks in a dataset of no fixed category images. Experiments declare our method can complete the image encryption/ decryption tasks in various scenarios. Additionally, the proposed approach exhibits broad generalization abilities with high encryption and decryption quality aided by the decryption total variation loss function. Compared to recently proposed deep learning-based image encryption approaches, our method demonstrates faster processing times for both image encryption and decryption, with at least a 2.7% and 7.5% increase in efficiency, respectively. Furthermore, our method improves decryption performance by at least 1.0% and 0.5% in Peak signal-to-ratio (PSNR) as well as structural similarity (SSIM) indicators while maintaining a high level of security. What is more, our method enhances traceability of data loss or noise attacks since such attacks leave a noticeable trail on decrypted images produced by our method.

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Data availability

All the data required for this research work, i.e., BSDS500 and Div2k dataset, are available publicly on the Berkley University of California Computer vision group and Div2k website. The data can be accessed from http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html#bench and https://data.vision.ee.ethz.ch/cvl/DIV2K/

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Acknowledgments

This study is supported by the Major Programs Incubation Plan of Xizang Minzu University “Research on multi-biometric characteristic image encryption method based on deep learning” under Grant 22MDZ03.

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Authors and Affiliations

  1. School of Information Engineering, Xizang Minzu University, Xianyang, Shaanxi, China

    Zhenjie Bao, Ru Xue, Jingyun Hu & Yue Liu

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  1. Zhenjie Bao
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Correspondence to Ru Xue.

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Bao, Z., Xue, R., Hu, J. et al. Color image encryption based on lite dense-ResNet and bit-XOR diffusion. Multimed Tools Appl 83, 12819–12848 (2024). https://doi.org/10.1007/s11042-023-16073-7

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