An Advanced Reversible Data Hiding Algorithm Using Local Similarity, Curved Surface Characteristics, and Edge Characteristics in Images

Abstract

In this paper, we proposed methods to accurately predict pixel values by effectively using local similarity, curved surface characteristics, and edge characteristics present in an image. Furthermore, to hide more confidential data in a cover image using the prediction image composed of precisely predicted pixel values, we proposed an effective data hiding technique that applied the prediction image to the conventional reversible data hiding technique. Precise prediction of pixel values greatly increases the frequency at the peak point in the histogram of the difference sequence generated using the cover and prediction images. This considerably increases the amount of confidential data that can be hidden in the cover image. The proposed reversible data hiding algorithm (ARDHA) can hide up to 24.5% more confidential data than the existing algorithm. Moreover, it is not possible to determine the presence of hidden confidential data in stego-images, as they possess excellent visual quality. The confidential data can be extracted from the stego-image without loss, and the original cover image can be restored from the stego-image without distortion. Therefore, the proposed algorithm can be effectively used in digital image watermarking, military, and medical applications.

Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video

“…For example, the integrity check code of a video can be embedded into it to assure the video used for law enforcement has not been modified. So far, many RDH schemes have been proposed, which can be classified into three main categories: lossless compression [1,2], difference expansion [3][4][5][6] and histogram shifting [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. HS-based RDH scheme was first proposed by Ni et al [7], and it is improved for years afterwards using the histograms of difference image [8,9] or prediction errors [10][11][12][13], multiple histograms [14][15][16][17] and 2D HS [18][19][20][21][22][23].…”