Adaptive Secure Image Transmission Using Tri-Layered Visual Cryptography and Residual Error Reduction for AI-Driven Interactive Mobile Learning Platforms

S. Shibani Raju; R. Thenmozhi

doi:10.3991/ijim.v20i12.62162

Authors

S. Shibani Raju SRM Institute of Science and Technology, Chengalpattu, India https://orcid.org/0009-0001-5825-7912
R. Thenmozhi SRM Institute of Science and Technology, Chengalpattu, India

DOI:

https://doi.org/10.3991/ijim.v20i12.62162

Keywords:

Visual Cryptography, Residual Error Reduction, Mobile Learning, Image Security, Deep Learning, Tri-Layered Encryption, PSNR, Adaptive Encoding, Privacy-Preserving Analytics, Secure Content Delivery

Abstract

As interactive mobile learning platforms based on artificial intelligence (AI) are being adopted rapidly, secure and efficient visual educational data communication have emerged as a crucial challenge. In this case, it is still important not to disclose any sensitive learning material (e.g., assessments, medical images and/or proprietary instructional media) while ensuring high visual fidelity for effective learning throughout the design process. In this study, we put forward an Adaptive Secure Image Transmission model using a tri-layered visual cryptography (TLVC) with the assistance of residual error reduction (RER) process. As such, the proposed system adopts a three-layer visual cryptographic method that breaks up input images into several insecure shares in which no single share contains anything of value by itself. Mobile-specific: Share generation can be dynamically tuned to the capability of device CPU, memory, network factors such as latency or available bandwidth. In order to combat more quality degradation caused by reconstruction, a RER module combines with lightweight deep residual learning is incorporated to improve the clarity of images and restore fine-grained structures. In addition, the framework’s integration with AI-based learning platforms allows for fast content delivery, interactive visualization of data, and privacy-preserving analytics that can be aggregated across multiple sources. Experimental evaluations prove that the proposed method guarantees a better security robustness and a more accurate reconstructed image in terms of mean squared error and peak signal-to-noise ratio in comparison to classical visual cryptography. The system also has low computational overhead and is adaptable to resource-scarce mobile devices.

References

[1] M. Naor and A. Shamir, 'Visual Cryptography,' in Advances in Cryptology — EUROCRYPT 1994, Lecture Notes in Computer Science, vol. 950, pp. 1–12, Springer, Berlin, Heidelberg, 1995.

[2] G. Ateniese, C. Blundo, A. De Santis, and D. R. Stinson, 'Visual Cryptography for General Access Structures,' Information and Computation, vol. 129, no. 2, pp. 86–106, 1996.

[3] R. Ito, H. Kuwakado, and H. Tanaka, 'Image Size Invariant Visual Cryptography,' IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E82-A, no. 10, pp. 2172–2177, 1999.

[4] C.-N. Yang, 'New Visual Secret Sharing Schemes Using Probabilistic Method,' Pattern Recognition Letters, vol. 25, no. 4, pp. 481–494, 2004.

[5] Z. Wang, G. R. Arce, and G. Di Crescenzo, 'Halftone Visual Cryptography via Error Diffusion,' IEEE Transactions on Information Forensics and Security, vol. 4, no. 3, pp. 383–396, 2009.

[6] J. Weir and W. Yan, 'A Comprehensive Study of Visual Cryptography,' Transactions on Data Hiding and Multimedia Security V, Lecture Notes in Computer Science, vol. 6010, pp. 70–105, Springer, 2010.

[7] J. Liu, T. Li, X. Peng, and J. Shao, 'Security Analysis of a Deep Learning-Based Visual Cryptography Using Generative Adversarial Networks,' IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3327–3339, 2021.

[8] M. Nakajima and Y. Yamaguchi, 'Extended Visual Cryptography for Natural Images,' Journal of WSCG, vol. 10, no. 2, pp. 303–310, 2002.

[9] S. Cimato, A. De Santis, A. L. Ferrara, and B. Masucci, 'Ideal Contrast Visual Cryptography Schemes with Reversing,' Information Processing Letters, vol. 93, no. 4, pp. 199–206, 2005.

[10] K. He, X. Zhang, S. Ren, and J. Sun, 'Deep Residual Learning for Image Recognition,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.

[11] K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, 'Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,' IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142–3155, 2017.

[12] Y. Chen and T. Pock, 'Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1256–1272, 2017.

[13] Y. Tai, J. Yang, X. Liu, and C. Xu, 'MemNet: A Persistent Memory Network for Image Restoration,' in Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 4539–4547, 2017.

[14] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, 'Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,' IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.

[15] J. F. Kurose and K. W. Ross, Computer Networking: A Top-Down Approach, 8th ed. Pearson Education, 2021.

[16] Z. Huang, T. Chen, X. Li, and Y. Yang, 'Privacy-Preserving Deep Learning with Adaptive Differential Privacy,' in Proceedings of the ACM Conference on Computer and Communications Security (CCS), pp. 1–14, 2022.

[17] L. Zhang, W. Zuo, S. Gu, and L. Zhang, 'Secure Progressive Image Delivery for Healthcare Mobile Applications Using Combined Visual Cryptography and JPEG2000,' IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 8, pp. 3890–3901, 2022.

[18] G. Siemens and P. Long, 'Penetrating the Fog: Analytics in Learning and Education,' EDUCAUSE Review, vol. 46, no. 5, pp. 30–40, 2011.

[19] L. Tuovinen, 'Privacy-Preserving Federated Learning for Educational Analytics in Intelligent Tutoring Systems,' IEEE Transactions on Learning Technologies, vol. 16, no. 1, pp. 88–102, 2023.

[20] D. J. Bernstein, 'ChaCha, a Variant of Salsa20,' in Workshop Record of SASC, vol. 8, pp. 3–5, 2008.

[21] A. Shamir, 'How to Share a Secret,' Communications of the ACM, vol. 22, no. 11, pp. 612–613, 1979.

[22] S. Lian, J. Sun, and Z. Wang, 'A Block Cipher Based on a Suitable Use of the Chaotic Standard Map,' Chaos, Solitons and Fractals, vol. 26, no. 1, pp. 117–129, 2005.

[23] C. Dong, C. C. Loy, K. He, and X. Tang, 'Image Super-Resolution Using Deep Convolutional Networks,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 295–307, 2016.

[24] O. Ronneberger, P. Fischer, and T. Brox, 'U-Net: Convolutional Networks for Biomedical Image Segmentation,' in Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lecture Notes in Computer Science, vol. 9351, pp. 234–241, Springer, 2015.

[25] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, 'Generative Adversarial Networks,' Communications of the ACM, vol. 63, no. 11, pp. 139–144, 2020.

[26] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, 'MobileNetV2: Inverted Residuals and Linear Bottlenecks,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510–4520, 2018.

[27] G. Hinton, O. Vinyals, and J. Dean, 'Distilling the Knowledge in a Neural Network,' arXiv preprint arXiv:1503.02531, 2015.

[28] C.A. Tavera Romero, P. del R. Segovia de Maya, M. F. Díaz Velásquez, and D. F. Pérez Carvajal, “Development and Impact of a Mobile Application that Allows Users to Track Their Location on an Educational Institution Campus”, Int. J. Interact. Mob. Technol., vol. 18, no. 01, pp. pp. 110–132, Jan. 2024.

[29] P. Sharma and A. K. Sinha, 'A Review on Image Encryption Techniques for Multimedia Security,' Multimedia Tools and Applications, vol. 79, no. 43, pp. 32229–32258, 2020.

[30] C. Blundo, A. De Bonis, and A. De Santis, 'Improved Schemes for Visual Cryptography,' Designs, Codes and Cryptography, vol. 24, no. 3, pp. 255–278, 2001.

[31] X. Wu and W. Sun, 'Random Grid-Based Visual Secret Sharing with Abilities of OR and XOR Decryptions,' Journal of Visual Communication and Image Representation, vol. 24, no. 1, pp. 48–62, 2013.

[32] M. Usman, I. Ahmed, M. I. Aslam, S. Khan, and U. A. Shah, 'SIT: A Lightweight Encryption Algorithm for Secure Internet of Things,' International Journal of Advanced Computer Science and Applications, vol. 8, no. 1, pp. 402–411, 2017.

[33] R. R. Rakotomalala, P.-A. Guidec, C. Ménard, and J. Tisseau, 'Adaptive Bitrate Streaming for Mobile Learning Environments: A Quality-of-Experience Driven Framework,' IEEE Access, vol. 10, pp. 74821–74838, 2022.

[34] A.Tao, “The Dynamics of Community Engagement in Distance Education: A Sociological Analysis Based on Online Learning Platforms”, Int. J. Interact. Mob. Technol., vol. 18, no. 07, pp. pp. 4–18, Apr. 2024.