Image Steganography With Symmetric Embedding Using Gaussian Markov Random Field Model

• Published in: IEEE transactions on circuits and systems for video technology Vol. 31; no. 3; pp. 1001 - 1015
• Main Authors: Su, Wenkang, Ni, Jiangqun, Hu, Xianglei, Fridrich, Jessica
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Distortion; Divergence; Fields (mathematics); Integrated circuit modeling; Iterative methods; KL-divergence; Markov random field (MRF); Markov random fields; minimal distortion embedding; multivariate Gaussian distribution; Optimization; Payloads; Random variables; Steganography

Recent advances on adaptive steganography show that the performance of image steganographic communication can be improved by incorporating the non-additive models that capture the dependencies among adjacent pixels. In this paper, a Gaussian Markov Random Field model (GMRF) with four-element cross n...