Trace Concealment Histogram-Shifting-Based Reversible Data Hiding with Improved Skipping Embedding and High-Precision Edge Predictor (ChinaMFS 2022)

Reversible data hiding (RDH) is a special class of steganography, in which the cover image can be perfectly recovered upon extraction secret data. However, most image-based RDH schemes focus on improving capacity–distortion performance. In this paper, we propose novel scheme not only effectively conceals traces left by HS but also improves First, high-precision edge predictor LS-ET (Least Square with Edge Type) proposed, and divides pixels into five types, i.e., weak edge, horizontal vertical positive diagonal negative edge. Different types target utilize different training stronger local consistency to improve accuracy. Then, prediction-based histogram-shifting (HS) framework designed conceal embedding stego images. Finally, both data-coding method skipping strategy quality. Experimental results demonstrate that performance proposed outperforms other trace concealment comparable state-of-the-art utilizing sorting technique, multiple histogram modification, excellent LS-based predictors. Moreover, it traditional certain extent, reducing risk being steganalyzed.