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Image Encryption Algorithm Based on Convolutional Neural Networks and Dynamic S-Box Generation

arXiv Security Archived Jun 19, 2026 ✓ Full text saved

arXiv:2606.20444v1 Announce Type: new Abstract: The paper proposes a dynamic approach to image encryption, combining the use of Convolutional Neural Networks (CNNs) and classical cryptography to improve the security and flexibility of image encryption. The main concept is to create adaptive Substitution boxes (S-boxes) based on characteristics that are learned by a trained CNN. The CNN-based S-boxes can be relied on for more non-linearity, uniqueness, and input image dependence than the conventi

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    Computer Science > Cryptography and Security [Submitted on 18 Jun 2026] Image Encryption Algorithm Based on Convolutional Neural Networks and Dynamic S-Box Generation Ans Ibrahim, Fadhil Abbas Fadhil, Mahameed Reza Feizi Derakhshi, Maryam Mahdi Alhusseini, Nikolai Safiullin The paper proposes a dynamic approach to image encryption, combining the use of Convolutional Neural Networks (CNNs) and classical cryptography to improve the security and flexibility of image encryption. The main concept is to create adaptive Substitution boxes (S-boxes) based on characteristics that are learned by a trained CNN. The CNN-based S-boxes can be relied on for more non-linearity, uniqueness, and input image dependence than the conventional fixed S-boxes because they are susceptible to the linear and differential attacks. This dynamic behaviour enhances the confusion property and makes it more resistant to statistical and structural attacks. The encryption algorithm consists of CNN-based feature extraction and the creation of a personalised S-box to replace the pixels. Entropy, histogram analysis, correlation, NPCR, and UACI enable security assessment of generated S-boxes based on the CNN, indicating that the scheme is more resilient and flexible than traditional ones. Subjects: Cryptography and Security (cs.CR); Software Engineering (cs.SE) Cite as: arXiv:2606.20444 [cs.CR]   (or arXiv:2606.20444v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.20444 Focus to learn more Submission history From: Maryam M. Alhusseini [view email] [v1] Thu, 18 Jun 2026 16:23:19 UTC (766 KB) Access Paper: view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 19, 2026
    Archived
    Jun 19, 2026
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