This paper proposes a reversible data embedding algorithm in encrypted images of cloud storage where the embedding was performed by detecting a predictor that provides a maximum embedding rate. Initially, the scheme generates trail data which are embedded using the prediction error expansion in the encrypted training images to obtain the embedding rate of a predictor. The process is repeated for different predictors from which the predictor that offers the maximum embedding rate is estimated. Using the estimated predictor as the label the Convolutional neural network (CNN) model is trained with the encrypted images. The trained CNN model is used to estimate the best predictor that provides the maximum embedding rate. The estimation of the best predictor from the test image does not use the trail data embedding process. The evaluation of proposed reversible data hiding uses the datasets namely BossBase and BOWS-2 with the metrics such as embedding rate, SSIM, and PSNR. The proposed predictor classification was evaluated with the metrics such as classification accuracy, recall, and precision. The predictor classification provides an accuracy, recall, and precision of 92.63\%, 91.73\%, and 90.13\% respectively. The reversible data hiding using the proposed predictor selection approach provides an embedding rate of 1.955 bpp with a PSNR and SSIM of 55.58dB and 0.9913 respectively.
[1] P. Bas, T. Filler, and T. Pevny´, Break Our Steganographic System”: The Ins and Outs of Organizing BOSS, Information Hiding, 6958 (2011), 59–70.
[2] P. Bas and T. Furon, Image database of BOWS-2, Available: http://bows2.eclille.fr.
[3] 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, 46(5) (2016), 1132–1143.
[4] 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, 58 (2019), 334–344.
[5] Y. Hu, H. -K. Lee, and J. Li, DE-Based Reversible Data Hiding With Improved Overflow Location Map, IEEE Transactions on Circuits and Systems for Video Technology, 19(2) (2009), 250–260.
[6] Jun Tian, Reversible data embedding using a difference expansion, IEEE Transactions on Circuits and Systems for Video Technology, 13(8) (2003), 890–896.
[7] H. J. Kim, V. Sachnev, Y. Q. Shi, J. Nam, and H. -G. Choo, A Novel Difference Expansion Transform for Reversible Data Embedding, IEEE Transactions on Information Forensics and Security, 3(3) (2008), 456–465.
[8] X. Li, B. Yang, and T. Zeng, Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel Selection, IEEE Transactions on Image Processing, 20(12) (2011), 3524–3533.
[9] 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, 8(3) (2013), 553–562.
[10] B. Ou, X. Li, W. Zhang, and Y. Zhao, Improving Pairwise PEE via Hybrid-Dimensional Histogram Generation and Adaptive Mapping Selection, IEEE Transactions on Circuits and Systems for Video Technology, 29(7) (2019), 2176–2190.
[11] 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, 22(12) (2013), 5010–5021.
[12] W. Puech, M. Chaumont, and O. Strauss, A Reversible Data Hiding Method for Encrypted Images, SPIE Electronic Imaging, Security, Forensics, Steganography, and Watermarking of Multimedia Contents, San Jose, CA, USA, (2008), 1–9.
[13] P. Puteaux, S. Ong, K. Wong, and W. Puech, A survey of reversible data hiding in encrypted images – The first 12 years, Journal of Visual Communication and Image Representation, 77 (2021), 103085.
[14] P. Puteaux and W. Puech, An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images, IEEE Transactions on Information Forensics and Security, 13(7) (2018), 1670–1681.
[15] P. Puteaux and W. Puech, A Recursive Reversible Data Hiding in Encrypted Images Method With a Very High Payload, IEEE Transactions on Multimedia, 23 (2021), 636–650.
[16] P. Puteaux and W. Puech, EPE-based Huge-Capacity Reversible Data Hiding in Encrypted Images, 018 IEEE International Workshop on Information Forensics and Security (WIFS), Hong Kong, China, (2018), 1–7.
[17] 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), Hong Kong, China , (2018), 1–7.
[18] Z. Qian, X. Zhang, and S. Wang, Reversible Data Hiding in Encrypted JPEG Bitstream, IEEE Transactions on Multimedia, 16(5) (2014), 1486–1491.
[19] A. Rosewelt and A. Renjit, Semantic analysis-based relevant data retrieval model using feature selection, summarization and CNN, Soft Computing, 24 (2020), 16983–17000.
[20] V. Sachnev, H. J. Kim, J. Nam, S. Suresh, and Y. Q. Shi, Reversible Watermarking Algorithm Using Sorting and Prediction, IEEE Transactions on Circuits and Systems for Video Technology, 19(7) (2009), 989–999.
[21] N. K. Sao, C. T. Luyen, and P. V. At, Efficient reversible data hiding using block histogram shifting with invariant peak points, J. Inf. Hiding Multim. Signal Process., 38(1) (2022), 78–97.
[22] K. P. S. Shijin and D. D. Edwin, Simulated attack based feature region selection for efficient digital image watermarking, 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), Nagercoil, India, (2012), 1128–1132.
[23] M. Suchetha, N. S. Ganesh, R. Raman, and D. E. Dhas, Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN, Soft Computing, 25 (2021), 15255–15268.
[24] D. M. Thodi and J. J. Rodriguez, Expansion Embedding Techniques for Reversible Watermarking, IEEE Transactions on Image Processing, 16(3) (2007), 721–730.
[25] M. Y. Valandar, M. J. Barani, P. Ayubi, and M. Aghazadeh, An integer wavelet transform image steganography method based on 3D sine chaotic map, Multimed Tools Appl, 78 (2019), 9971–9989.
[26] W. Weiqing, Y. Junyong, W. Tongqing, and W. Weifu, A high capacity reversible data hiding scheme based on right-left shift, Signal Processing, 150 (2018), 102–115.
[27] C.-H. Yang and M.-H. Tsai, Improving histogram-based reversible data hiding by interleaving predictions, IET Image Processing, 4(4) (2010), 223–234.
[28] M. Yesilyurt and Y. Yalman, New approach for ensuring cloud computing security: using data hiding methods, S¯adhan¯a, 41 (2016), 1289–1298.
[29] S. Yi and Y. Zhou, Separable and Reversible Data Hiding in Encrypted Images Using Parametric Binary Tree Labeling, IEEE Transactions on Multimedia, 20(1) (2019), 51–64.
[30] X. Zhang, J. Long, Z. Wang, and H. Cheng, Lossless and Reversible Data Hiding in Encrypted Images With Public-Key Cryptography, IEEE Transactions on Circuits and Systems for Video Technology, 26(9) (2016), 1622– 1631.
[31] A. Zulehner and R. Wille, Make it reversible: Efficient embedding of non-reversible functions, Design, Automation & Test in Europe Conference & Exhibition (DATE), (2017), 458–463.
CN, P., & Suchithra, R. (2024). Convolutional neural network-based high capacity predictor estimation for reversible data embedding in cloud network. Computational Methods for Differential Equations, 12(3), 585-598. doi: 10.22034/cmde.2023.59134.2512
MLA
Prasad CN; R Suchithra. "Convolutional neural network-based high capacity predictor estimation for reversible data embedding in cloud network". Computational Methods for Differential Equations, 12, 3, 2024, 585-598. doi: 10.22034/cmde.2023.59134.2512
HARVARD
CN, P., Suchithra, R. (2024). 'Convolutional neural network-based high capacity predictor estimation for reversible data embedding in cloud network', Computational Methods for Differential Equations, 12(3), pp. 585-598. doi: 10.22034/cmde.2023.59134.2512
VANCOUVER
CN, P., Suchithra, R. Convolutional neural network-based high capacity predictor estimation for reversible data embedding in cloud network. Computational Methods for Differential Equations, 2024; 12(3): 585-598. doi: 10.22034/cmde.2023.59134.2512
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