Reversible Data Hiding Based on Pixel-Value-Ordering and Pixel Block Merging Strategy

Abstract

With the reversible data hiding method based on pixel-value-ordering, data are embedded through the modification of the maximum and minimum values of a block. A significant relationship exists between the embedding performance and the block size. Traditional pixel-value-ordering methods utilize pixel blocks with a fixed size to embed data; the smaller the pixel blocks, greater is the embedding capacity. However, it tends to result in the deterioration of the quality of the marked image. Herein, a novel reversible data hiding method is proposed by incorporating a block merging strategy into Li et al.'s pixel-value-ordering method, which realizes the dynamic control of block size by considering the image texture. First, the cover image is divided into non-overlapping 2×2 pixel blocks. Subsequently, according to their complexity, similarity and thresholds, these blocks are employed for data embedding through the pixel-value-ordering method directly or after being emerged into 2×4, 4×2, or 4×4 sized blocks. Hence, smaller blocks can be used in the smooth region to create a high embedding capacity and larger blocks in the texture region to maintain a high peak signal-to-noise ratio. Experimental results prove that the proposed method is superior to the other three advanced methods. It achieves a high embedding capacity while maintaining low distortion and improves the embedding performance of the pixel-value-ordering algorithm.

Resampling Factor Estimation via Dual-stream Convolutional Neural Network

“…Nowadays, with the emergence of convenient and easy-to-use image processing software, non-experts can easily edit and manipulate digital images without leaving obvious perceptible artifacts, which degrades the authority of digital images as an evidence for criminal investigation and legal proceedings. Besides the changes of the image content, some illegal information could be embedded in a digital image [1,2]. Although tampered images usually maintain high visual quality, image manipulation generally destroys inherent statistical consistency of tampered images and leaves unique traces.…”