TL;DR
This paper introduces two multi-MSB replacement-based methods for reversible data hiding in encrypted images, achieving high embedding capacity, improved speed, and better image quality, with one method enabling lossless recovery.
Contribution
The paper presents novel multi-MSB replacement techniques for RDHEI that outperform existing methods in capacity, speed, and image quality, including a lossless recovery approach.
Findings
EMR-RDHEI achieves 1.2087 to 6.2682 bpp payloads
LMR-RDHEI achieves an average of 2.5325 bpp payload
Both methods outperform state-of-the-art RDHEI algorithms
Abstract
As an essential technique for data privacy protection, reversible data hiding in encrypted images (RDHEI) methods have drawn intensive research interest in recent years. In response to the increasing demand for protecting data privacy, novel methods that perform RDHEI are continually being developed. We propose two effective multi-MSB (most significant bit) replacement-based approaches that yield comparably high data embedding capacity, improve overall processing speed, and enhance reconstructed images' quality. Our first method, Efficient Multi-MSB Replacement-RDHEI (EMR-RDHEI), obtains higher data embedding rates (DERs, also known as payloads) and better visual quality in reconstructed images when compared with many other state-of-the-art methods. Our second method, Lossless Multi-MSB Replacement-RDHEI (LMR-RDHEI), can losslessly recover original images after an information embedding…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
