Reversible Data Hiding in Encrypted Images Based on Progressive Recovery | Matlab IEEE Project
Reversible Data Hiding in Encrypted Images Based on Progressive Recovery | Matlab Image Processing Final Year IEEE project with source code. To buy this proj...
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Reversible Data Hiding in Encrypted Images Based on Progressive Recovery | Matlab Image Processing Final Year IEEE project with source code.
To buy this project in ONLINE, Contact:
🔗Email: jpinfotechprojects@gmail.com,
🌐Website: https://www.jpinfotech.org
🚀IEEE Base Paper Title: Reversible Data Hiding in Encrypted Images Based on Progressive Recovery.
💡Implementation Code: MATLAB.
🛠️Tool details: MATLABR2013A.
💰Cost (In Indian Rupees): Rs.3000/
📘IEEE Base paper Abstract:
In this paper, we propose an Enhanced Immense Capacity Reversible Image Hiding with Color Images. As an enhancement of the existing work, in this paper we introduce novel features of including Color Images. Where in the existing system, the system used gray scale images, which also provided only small capacity. The Reversible data hiding algorithm employed in the proposed model leads to more data capacity in addition to improved contrast cover image. In addition to it, we introduce Image Hiding with more security included by encrypted image is hided into another image (Using Steganography Concept). The proposed image encryption method keeps the file structure maximum unchanged. Our proposal also introduces the novelty in evaluation parameter from the existing system by adding more performance metrics such as PSNR, MSE, SSIM, NC and Error Rate, Bit error rate. Processing the proposed technique is not much complex than the existing system, leading to less computational time too. Experimental results carried out using Matlab with various grayscale and color images will illustrate the excellence of the proposed model.
📚REFERENCE:
Zhenxing Qian, Member, IEEE, Xinpeng Zhang, Member, IEEE, Guorui Feng, Member, IEEE, “Reversible Data Hiding in Encrypted Images Based on Progressive Recovery”, IEEE 2016.
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Published
Oct 12, 2017
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