High-Quality Reversible Data Hiding Based on Multi-Embedding for Binary Images

Xiang Li, Xiaolong Li (), Mengyao Xiao, Yao Zhao and Hsunfang Cho
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Xiang Li: Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
Xiaolong Li: Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
Mengyao Xiao: Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
Yao Zhao: Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
Hsunfang Cho: Shanghai Data Exchange Corporation, Shanghai 201203, China

Mathematics, 2023, vol. 11, issue 19, 1-11

Abstract: Unlike histogram-based reversible data hiding (RDH), the general distortion-based framework considers pixel-by-pixel distortions, which is a new research direction in RDH. The advantage of the general distortion-based RDH method is that it can enhance the visual quality of the marked image by embedding data into visually insensitive regions (e.g., edges and textures). In this paper, following this direction, a high-capacity RDH approach based on multi-embedding is proposed. The cover image is decoupled to select the embedding sequence that can better utilize texture pixels and reduce the size of the reconstruction information, and a multi-embedding strategy is proposed to embed the secret data along with the reconstruction information by matrix embedding. The experimental results demonstrate that the proposed method provides a superior visual quality and higher embedding capacity than some state-of-the-art RDH works for binary images. With an embedding capacity of 1000 bits, the proposed method achieves an average PSNR of 49.45 dB and an average SSIM of 0.9705 on the test images. This marks an improvement of 1.1 dB in PSNR and 0.0242 in SSIM compared to the latest state-of-the-art RDH method.

Keywords: reversible data hiding; general distortion model; multi-embedding; binary images (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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