Journal of Machine and Computing


Fully Connected Neural Network Based Carrier Estimation Mechanism on Encrypted Images for Data Hiding in Cloud Network



Journal of Machine and Computing

Received On : 05 March 2024

Revised On : 14 November 2024

Accepted On : 21 February 2025

Published On : 05 April 2025

Volume 05, Issue 02

Pages : 878-889


Abstract


The work proposes a fully connected neural network (FCNN) based approach for detecting the Carrier blocks for embedding the data in encrypted images in the cloud network. In a data embedding process, the determination of non-carrier pixels that provide underflow and overflow during the data embedding process plays a major role. The location map for the non-carrier blocks is usually compressed and embedded in the encrypted image along with the hidden data. The embedding rate and peak signal-to-noise ratio (PSNR) are limited due to the storage of huge location map information on the image. Therefore, the proposed approach uses the FCNN network to detect the Carrier blocks /non-carrier blocks which highly minimizes the additional location map information to be embedded. In the embedding phase, a trained FCNN network is utilized to detect the carrier blocks, in which the FCNN network is trained with the labels that are generated by trial 0’s and 1’s embedding process. Two approaches are utilized in training the FCNN that includes FCNN with predictor only (FCNN-PO) and FCNN with sub-block fully (FCNN-SF) schemes in detecting the carrier blocks. In the extraction phase, the same FCNN model is used to detect the carrier blocks from which the data and actual encrypted image can be reconstructed. The performance of the carrier detection process was evaluated using measures such as precision, recall, and accuracy, while the data hiding process was evaluated using measures such as structural similarity index measurement (SSIM), PSNR, and embedding rate. The FCNN-PO carrier/non-carrier classification process results in an average accuracy of 98.59% in detecting the carrier while providing an SSIM, PSNR, and embedding rate of 0.9926, 58.86dB and 1.97bpp respectively during the data embedding process when evaluated using the Bows-2 dataset.


Keywords


Data Hiding, Fully Connected Neural Network, Image Encryption, Embedding Rate, Prediction Error Expansion.


  1. M. Yesilyurt and Y. Yalman, “New approach for ensuring cloud computing security: using data hiding methods,” Sādhanā, vol. 41, no. 11, pp. 1289–1298, Nov. 2016, doi: 10.1007/s12046-016-0558-8.
  2. K. P. S. Shijin and Dhas. D. Edwin, “Simulated attack-based feature region selection for efficient digital image watermarking,” 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), pp. 1128–1132, Mar. 2012, doi: 10.1109/icceet.2012.6203846.
  3. M. Y. Valandar, M. J. Barani, P. Ayubi, and M. Aghazadeh, “An integer wavelet transforms image steganography method based on 3D sine chaotic map,” Multimedia Tools and Applications, vol. 78, no. 8, pp. 9971–9989, Sep. 2018, doi: 10.1007/s11042-018-6584-2.
  4. Sao, Nguyen Kim, Cao Thi Luyen, and Pham Van At. "Efficient reversible data hiding using block histogram shifting with invariant peak points." J. Inf. Hiding Multim. Signal Process. 13.1 (2022): 78-97.
  5. A. Zulehner and R. Wille, “Make it reversible: Efficient embedding of non-reversible functions,” Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, pp. 458–463, Mar. 2017, doi: 10.23919/date.2017.7927033.
  6. X. Cao, L. Du, X. Wei, D. Meng, and X. Guo, “High-Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation,” IEEE Transactions on Cybernetics, vol. 46, no. 5, pp. 1132–1143, May 2016, doi: 10.1109/tcyb.2015.2423678.
  7. K. Chen and C.-C. Chang, “High-capacity reversible data hiding in encrypted images based on extended run-length coding and block-based MSB plane rearrangement,” Journal of Visual Communication and Image Representation, vol. 58, pp. 334–344, Jan. 2019, doi: 10.1016/j.jvcir.2018.12.023.
  8. K. Ma, W. Zhang, X. Zhao, N. Yu, and F. Li, “Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption,” IEEE Transactions on Information Forensics and Security, vol. 8, no. 3, pp. 553–562, Mar. 2013, doi: 10.1109/tifs.2013.2248725.
  9. Y. Puyang, Z. Yin, and Z. Qian, “Reversible Data Hiding in Encrypted Images with Two-MSB Prediction,” 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–7, Dec. 2018, doi: 10.1109/wifs.2018.8630785.
  10. Jun Tian, “Reversible data embedding using a difference expansion,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 13, no. 8, pp. 890–896, Aug. 2003, doi: 10.1109/tcsvt.2003.815962.
  11. D. M. Thodi and J. J. Rodriguez, “Expansion Embedding Techniques for Reversible Watermarking,” IEEE Transactions on Image Processing, vol. 16, no. 3, pp. 721–730, Mar. 2007, doi: 10.1109/tip.2006.891046.
  12. B. Ou, X. Li, Y. Zhao, R. Ni, and Y.-Q. Shi, “Pairwise Prediction-Error Expansion for Efficient Reversible Data Hiding,” IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 5010–5021, Dec. 2013, doi: 10.1109/tip.2013.2281422.
  13. Xiaolong Li, Bin Yang, and Tieyong Zeng, “Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel Selection,” IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3524–3533, Dec. 2011, doi: 10.1109/tip.2011.2150233.
  14. V. Sachnev, Hyoung Joong Kim, Jeho Nam, S. Suresh, and Yun Qing Shi, “Reversible Watermarking Algorithm Using Sorting and Prediction,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 19, no. 7, pp. 989–999, Jul. 2009, doi: 10.1109/tcsvt.2009.2020257.
  15. W. Wang, J. Ye, T. Wang, and W. Wang, “A high-capacity reversible data hiding scheme based on right-left shift,” Signal Processing, vol. 150, pp. 102–115, Sep. 2018, doi: 10.1016/j.sigpro.2018.04.008.
  16. Z. Fu, X. Chai, Z. Tang, X. He, Z. Gan, and G. Cao, “Adaptive embedding combining LBE and IBBE for high-capacity reversible data hiding in encrypted images,” Signal Processing, vol. 216, p. 109299, Mar. 2024, doi: 10.1016/j.sigpro.2023.109299.
  17. H. Gao, X. Zhang, and T. Gao, “Hierarchical reversible data hiding in encrypted images based on multiple linear regressions and multiple bits prediction,” Multimedia Tools and Applications, vol. 83, no. 3, pp. 8757–8783, Jun. 2023, doi: 10.1007/s11042-023-15939-0.
  18. W. He and Z. Cai, “An Insight into Pixel Value Ordering Prediction Based Prediction-error Expansion,” IEEE Transactions on Information Forensics and Security, pp. 1–1, 2020, doi: 10.1109/tifs.2020.3002377.
  19. H. Wang et al., “A high-precision arrhythmia classification method based on dual fully connected neural network,” Biomedical Signal Processing and Control, vol. 58, p. 101874, Apr. 2020, doi: 10.1016/j.bspc.2020.101874.
  20. J. Naren and A. R. Babu, “EEG stress classification based on Doppler spectral features for ensemble 1D-CNN with LCL activation function,” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 4, p. 102013, Apr. 2024, doi: 10.1016/j.jksuci.2024.102013.
  21. P. Bas, T. Filler, and T. Pevný, “” Break Our Steganographic System”: The Ins and Outs of Organizing BOSS,” Information Hiding, pp. 5970, 2011, doi: 10.1007/978-3-642-24178-9_5.

CRediT Author Statement


The authors confirm contribution to the paper as follows:

Conceptualization: Prasad C N and Suchithra R; Methodology: Prasad C N and Suchithra R; Software: Prasad C N; Data Curation: Prasad C N and Suchithra R; Writing- Original Draft Preparation: Prasad C N and Suchithra R; Investigation: Prasad C N and Suchithra R; Supervision: Suchithra R; Validation: Prasad C N and Suchithra R; Writing- Reviewing and Editing: Prasad C N and Suchithra R; All authors reviewed the results and approved the final version of the manuscript.


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Author(s) thanks to Dr. Suchithra R for this research completion and support.


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Cite this article


Prasad C N and Suchithra R, “Fully Connected Neural Network Based Carrier Estimation Mechanism on Encrypted Images for Data Hiding in Cloud Network”, Journal of Machine and Computing, pp. 878-889, April 2025, doi: 10.53759/7669/jmc202505069.


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© 2025 Prasad C N and Suchithra R. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.