Double image encryption algorithm based on neural network and chaos
Open accessAbstract
To realize the secure transmission of double images, this paper proposes a double image encryption algorithm based on convolutional neural network (CNN) and dynamic adaptive diffusion. This scheme is different from the existing double image encryption technology. According to the characteristics of digital image, we design a dual-channel (digital channel / optical channel) encryption method, which not only ensures the security of double image, but also improves the encryption efficiency and reduces the possibility of being attacked. First, a chaotic map is used to control the initial values of the 5D conservative chaotic system to enhance the security of the key. Secondary, in order to effectively resist known-plaintext attack and chosen-plaintext attack, we employ a chaotic sequence as convolution kernel of convolution neural network to generate plaintext related chaotic pointer to control the scrambling operation of two images. On this basis, a novel image fusion method is designed, which splits and fuses two images into two different parts according to the amount of information contained. In addition, a dual-channel image encryption scheme, optical encryption channel and digital encryption channel, is designed for the two parts after fusion. The former has better parallelism and higher encryption efficiency, while the latter has higher computational complexity and better encryption reliability. Especially in the digital encryption channel, a new dynamic adaptive diffusion method is designed, which is more flexible and secure than the existing encryption algorithm. Finally, numerical simulation and experimental analysis verify the feasibility and effectiveness of the scheme.
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Neural networks can be used to permute and/or compress plaintext images.
“Neural networks can be used to permute [35] and/or compress the plaintext images [36].”
From Image Encryption Algorithms: A Survey of Design and Evaluation Metrics (2024) · cites this paper as [35]
Zhenlong Man and Jinqing Li [130] first tried to combine a convolutional neural network (CONN) and plaintext correlation scrambling mechanism and proposed double image encryption with a CONN and dynamic adaptive diffusion.
“Zhenlong Man and Jinqing Li [130] first tried to combine a convolutional neural network (CONN) and plaintext correlation scrambling mechanism and proposed double image encryption with a CONN and dynamic adaptive diffusion.”
From Chaos-Based Image Encryption: Review, Application, and Challenges (2023) · cites this paper as [130]
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“Man et al. [35] proposed a double image encryption algorithm based on the convolution neural network (CNN) and chaos.”
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- Authors
- Man, Z., Li, J., Di, X., Sheng, Y., & Liu, Z.
- Affiliations
- Changchun University of Science and Technology (CN)
- Corresponding author
- Li, Jinqing
- Di, Xiaoqiang
- Venue
- Chaos, Solitons & Fractals
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 152
- Pages
- 111318
- ISSN
- 0960-0779 (ISSN-L)
- OpenAlex
- W3194478349
- Cited by
- 241
- References
- 67
- Citations by year
- 2026: 23 · 2025: 52 · 2024: 71 · 2023: 56 · 2022: 39
- FWCI
- 14.94
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Chaos Solitons & Fractals (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- People's Government of Jilin Province
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Keywords
- double image encryption, convolutional neural networks, chaotic maps, image fusion, image scrambling, known-plaintext attack resistance, chosen-plaintext attack resistance, key security
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