Improving histogram-based reversible data hiding by interleaving predictions

Abstract

Data hiding is an important way of realising copyright protection for multimedia. In this study, a new predictive method is proposed to enhance the histogram-based reversible data hiding approach on grey images. In those developed histogram-based reversible data hiding approaches, their drawbacks are the number of predictive values less to the number of pixels in an image. In these interleaving prediction methods, the predictive values are as many as the pixel values. All predictive error values are transformed into histogram to create higher peak values and to improve the embedding capacity. Moreover, for each pixel, its difference value between the original image and the stego-image remains within +1. This guarantees that the peak signal-to-noise ratio (PSNR) of the stego-image is above 48 dB. Experimental results show that the histogrambased reversible data hiding approach can raise a larger capacity and still remain a good image quality, compared to other histogram-based approaches.

Robust Lossless Data Hiding by Feature-Based Bit Embedding Algorithm

“…For comparison, three outstanding approaches: Wu et al's scheme (Wu et al 2009), Lee et al's algorithm (Lee et al, 2010), and Yang & Tsai's technique (Yang & Tsai, 2010) were compared with our method. Performance comparison between these methods was given in Table 3.…”

Image adaptive DCT- SVD based digital watermarking scheme by human visual characteristics

“…Also, the algorithm attaches only binary watermark images and it became computationally inefficient, since it used vector quantization algorithm for the optimization of adaptive quantization parameters, while indexing the blocks. Many other algorithms (Bhosale et al, 2012;Chen et al, 2012;Coatrieux et al, 2012;Ishikawa et al, 2012;Kandpal et al, 2012;Vargas et al, 2013;Varghese et al, 2014;Walia et al, 2013;Wang et al, 2011;Wang et al, 2012;Wu et al, 2005;Yang et al, 2010;Zareian et al, 2013) also developed in the literature by meeting some requirements of watermarking, but these algorithms also failed in simultaneously meeting other vital requirements of watermarking (Mason et al, 2000) including perceptibility, robustness, orthogonality, protection against attacks etc.…”

A Lossless Data Hiding Strategy Based on Two-Dimensional Side-Match Predictions

“…For example of 1-bit LSBs method, a secret bit is to be embedded into 1-rightmost bit of the cover pixel. The other is reversible data hiding (RDH) scheme [5][6][7][8][9][10][11][12][13][14][15][16]. The goal of RDH algorithm is to obtain both cover media and hidden data that can be recovered and extracted from stego media.…”

“…Many RDH techniques have been developed in present. It can be divided into three categories, namely, histogram-based [5][6][7][8], difference expansion [9][10][11][12], and prediction error [13][14][15][16]. Ni et al first proposed a reversible data hiding based on histogram shifting in 2006 [5].…”