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 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.

Reversible Data Hiding: Advances in the Past Two Decades

A novel lossless color image encryption scheme using 2D DWT and 6D hyperchaotic system

Histogram-based lossless data embedding (LDE) has been recognized as an effective and efficient way for copyright protection of multimedia. Recently, a LDE method using the statistical quantity histogram has achieved good performance, which utilizes the similarity of the arithmetic average of difference histogram (AADH) to reduce the diversity of images and ensure the stable performance of LDE. However, this method is strongly dependent on some assumptions, which limits its applications in practice. In addition, the capacities of the images with the flat AADH, e.g., texture images, are a little bit low. For this purpose, we develop a novel framework for LDE by incorporating the merits from the generalized statistical quantity histogram (GSQH) and the histogram-based embedding. Algorithmically, we design the GSQH driven LDE framework carefully so that it: (1) utilizes the similarity and sparsity of GSQH to construct an efficient embedding carrier, leading to a general and stable framework; (2) is widely adaptable for different kinds of images, due to the usage of the divide-and-conquer strategy; (3) is scalable for different capacity requirements and avoids the capacity problems caused by the flat histogram distribution; (4) is conditionally robust against JPEG compression under a suitable scale factor; and (5) is secure for copyright protection because of the safe storage and transmission of side information. Thorough experiments over three kinds of images demonstrate the effectiveness of the proposed framework.

Lossless Data Embedding Using Generalized Statistical Quantity Histogram

Image encryption

Robust reversible watermarking (RRW) methods are popular in multimedia for protecting copyright, while preserving intactness of host images and providing robustness against unintentional attacks. However, conventional RRW methods are not readily applicable in practice. That is mainly because: 1) they fail to offer satisfactory reversibility on large-scale image datasets; 2) they have limited robustness in extracting watermarks from the watermarked images destroyed by different unintentional attacks; and 3) some of them suffer from extremely poor invisibility for watermarked images. Therefore, it is necessary to have a framework to address these three problems, and further improve its performance. This paper presents a novel pragmatic framework, wavelet-domain statistical quantity histogram shifting and clustering (WSQH-SC). Compared with conventional methods, WSQH-SC ingeniously constructs new watermark embedding and extraction procedures by histogram shifting and clustering, which are important for improving robustness and reducing run-time complexity. Additionally, WSQH-SC includes the property-inspired pixel adjustment to effectively handle overflow and underflow of pixels. This results in satisfactory reversibility and invisibility. Furthermore, to increase its practical applicability, WSQH-SC designs an enhanced pixel-wise masking to balance robustness and invisibility. We perform extensive experiments over natural, medical, and synthetic aperture radar images to show the effectiveness of WSQH-SC by comparing with the histogram rotation-based and histogram distribution constrained methods.

Robust Reversible Watermarking via Clustering and Enhanced Pixel-Wise Masking

Content-adaptive reliable robust lossless data embedding

Robust lossless data hiding (LDH) methods have attracted more and more attentions for copyright protection of multimedia in lossy environment. One of the important requirements of the robust LDH methods is the reversibility, that is, the host images can be recovered without any distortion after the hidden messages are removed. The reversibility is often guaranteed by the embedding model, which affects the performance of the methods greatly. Another requirement is to have a better robustness, which allows the LDH methods to be well adaptable to the lossless and lossy environment, e.g., JPEG compression. In this paper, we firstly categorize the existing robust LDH methods according to the embedding model, and then make a theoretical analysis on the performance in terms of capacity, invisibility and the robustness. Finally, experimental comparisons are carried out to summarize the advantages and disadvantages of each kind of method.

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Robust lossless data hiding: Analysis and evaluation

The histogram shifting based reversible watermarking techniques have attracted increasing interests due to their low computational complexity, high visual quality and considerable capacity. However, those methods suffer from unstable performance because they fail to consider the diversity of grayscale histograms for various images. For this purpose, we develop a novel histogram shifting based method by introducing a block statistical quantity (BSQ). The similarity of BSQ distributions for different images reduces the diversity of grayscale histograms and guarantees the stable performance of the proposed method. We also adopt different embedding schemes to prevent the issues of overflow and underflow. Moreover, by selecting the block size, the capacity of the proposed watermarking scheme becomes adjustable. The experimental results of performance comparisons with other existing methods are provided to demonstrate the superiority of the proposed method.

Reversible Watermarking Based on Statistical Quantity Histogram

Desynchronization attacks resilient image watermarking scheme based on global restoration and local embedding

In the image watermarking community, developing a robust watermarking method has attracted increasing attention in recent years. However, protecting an image watermarking system against geometric distortions and common image processing operations with blind detection is still challenging. In this paper, a robust image watermarking scheme is proposed based on histogram modification since the histogram of a host image is approximately invariant to scaling and statistically independent to the position of pixels in the image plane. In the embedding end, the histogram of the host image is first computed and the bins with large population of pixels are then selected by a predefined threshold to form the appropriate embedding range. The watermark is then embedded in the host image by modifying the consecutive triple of bins belonging to the embedding range. In the detection end, global search incorporated with local search is exploited to retrieve the watermark. Experimental results confirm that the proposed image watermarking scheme can resist against geometric distortions as well as common image processing operations and outperforms the representative histogram-based image watermarking in terms of robustness.

Feng Ji

Papers

Content-adaptive reliable robust lossless data embedding

Robust lossless data hiding: Analysis and evaluation

Reversible Watermarking Based on Statistical Quantity Histogram

Desynchronization attacks resilient image watermarking scheme based on global restoration and local embedding

Histogram modification based robust image watermarking approach