No abstract

In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.

A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.

In the past two decades, reversible data hiding (RDH), also referred to as lossless or invertible data hiding, has gradually become a very active research area in the field of data hiding. This has been verified by more and more papers on increasingly wide-spread subjects in the field of RDH research that have been published these days. In this survey paper the various RDH algorithms and researches have been classified into the following six categories: 1) RDH into image spatial domain, 2) RDH into image compressed domain (e.g., JPEG), 3) RDH suitable for image semi-fragile authentication, 4) RDH with image contrast enhancement, 5) RDH into encrypted images, which is expected to have wide application in the cloud computation, and 6) RDH into video and into audio. For each of these six categories, the history of technical developments, the current state of the arts, and the possible future researches are presented and discussed. It is expected that the RDH technology and its applications in the real word will continue to move ahead.

Uniform embedding was first introduced in 2012 for non-side-informed JPEG steganography, and then extended to the side-informed JPEG steganography in 2014. The idea behind uniform embedding is that, by uniformly spreading the embedding modifications to the quantized discrete cosine transform (DCT) coefficients of all possible magnitudes, the average changes of the first-order and the second-order statistics can be possibly minimized, which leads to less statistical detectability. The purpose of this paper is to refine the uniform embedding by considering the relative changes of statistical model for digital images, aiming to make the embedding modifications to be proportional to the coefficient of variation. Such a new strategy can be regarded as generalized uniform embedding in substantial sense. Compared with the original uniform embedding distortion (UED), the proposed method uses all the DCT coefficients (including the DC, zero, and non-zero AC coefficients) as the cover elements. We call the corresponding distortion function uniform embedding revisited distortion (UERD), which incorporates the complexities of both the DCT block and the DCT mode of each DCT coefficient (i.e., selection channel), and can be directly derived from the DCT domain. The effectiveness of the proposed scheme is verified with the evidence obtained from the exhaustive experiments using a popular steganalyzer with rich models on the BOSSbase database. The proposed UERD gains a significant performance improvement in terms of secure embedding capacity when compared with the original UED, and rivals the current state-of-the-art with much reduced computational complexity.