High Capacity Reversible Data Hiding in Encrypted Images by Recursive Look-Ahead Adaptive Msb Prediction for Cloud Computing

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL High Capacity Reversible Data Hiding in Encrypted Images by Recursive Look-Ahead Adaptive Msb Prediction for Cloud Computing 25 Pages Posted: 27 Aug 2022 See all articles by selwyn Paul Jselwyn Paul Jaffiliation not provided to SSRN Abstract This paper proposes a high-capacity reversible data embedding algorithm in encrypted images (RDH-EI) that uses a recursive look ahead adaptive MSB prediction approach for Cloud storage applications. The paper also proposes a three-level image encryption approach where in the first level, the image is subdivided into [[EQUATION]] blocks and the blocks are permuted. In the second level, two different neighbourhoods namely the middle neighbourhood and peripheral neighbourhood are estimated, from which the elements of the middle neighbourhood are permuted within the respective neighbourhood. In the third level, the elements of the peripheral neighbourhood without including the elements of the middle neighbourhood are permuted within the respective neighbourhood. The image that was encrypted by the image owner was uploaded to the cloud. Out of five different neighbourhoods estimated on the [[EQUATION]] blocks, the best neighbourhood is then estimated from the encrypted image which provides a maximum embedding capacity. The data is then embedded in the best neighbourhood using the adaptive MSB prediction algorithm. Once the embedding iteration is completed, the marked encrypted image obtained on the previous iteration is again evaluated for best neighbourhood. This recursive process is repeated till the embedding capacity of the best neighbourhood is less than the threshold. The evaluation of the proposed algorithm was done using the standard test images obtained from the datasets namely BOSSbase, UCID and BOWS-2 using the metrics such as embedding rate, SSIM and PSNR. The proposed approach provides a average embedding rate of 3.714bpp, 3.4826bpp and 2.9412bpp for the datasets BOSSbase, BOWS-2 and UCID respectively with the number of recursive processes [[EQUATION]] which is higher than other recent RDH-EI schemes. Keywords: Reversible data hiding, permutation, Encrypted images, a recursive process, adaptive MSB predictor Suggested Citation: Suggested Citation J, selwyn Paul, High Capacity Reversible Data Hiding in Encrypted Images by Recursive Look-Ahead Adaptive Msb Prediction for Cloud Computing. Available at SSRN: https://ssrn.com/abstract=4202301 Selwyn Paul J (Contact Author) affiliation not provided to SSRN ( email ) No Address Available Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 0 Abstract Views 5 PlumX Metrics Related eJournals Other Information Systems & eBusiness eJournal Follow Other Information Systems & eBusiness eJournal Subscribe to this fee journal for more curated articles on this topic FOLLOWERS 1 PAPERS 6,570 Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo5 in 0.218 seconds