Abstract Massive databases encounter security such as data theft, illegal copying, and copyright infringement during creating, transmitting, and sharing of big data. The reversible data watermarking technology can effectively solve these problems, which can extract the watermark information accurately and recover the original carrier data without any distortion. However, most existing methods extract watermark information non-blindly and cannot effectively achieve a balance between watermark embedding capacity and data distortion. This paper proposes a blind reversible database watermarking method based on dual embedding, which combines histogram shifting and distortion-free watermarking methods to achieve an adaptive selection of histogram bins, blind extraction of watermark information, and carrier data recovery. The proposed method preprocesses the database tuples by scrambling them and constructs a prediction error histogram using first-layer tuples in square prediction within each group. The watermark information is embedded through adaptive selection and expansion of histogram bins, while the distortion-free watermarking method is used in another layer to assist in the recovery of original carrier data. The experimental results show that the proposed method can achieve an embedding capacity of more than three times the capacity of existing methods. It can also achieve blind watermark extraction and outperform some other state-of-the-art methods.

Database watermarking is typically employed to address the issues of data theft, illegal replication, and copyright infringement that may arise during the sharing of databases. Unfortunately, the existing methods often cause permanent distortion to the original data, and it is challenging to strike a balance between the watermark embedding capacity and data distortion. Therefore, this paper proposes a reversible database watermarking method based on histogram shifting, rhombus prediction, and double embedding with high capacity and low distortion, called RPDE-HSW. By utilizing the rhombus prediction, we respectively constructed two prediction error histograms in each subgroup and expanded the watermark capacity through the adoption of double-layer embedding and single-bin embedding 2 bits. A scrambling algorithm is used to make the attribute value distribution more discretized, resulting in a sparse distribution of the database histogram. Subsequently, we optimized the selection rules for the watermark embedding carrier, effectively eliminating the redundant distortion caused by histogram shifting. Experimental results demonstrate that the proposed method achieves smaller data distortion and higher watermark embedding capacity, outperforming some other state-of-the-art works, and does not affect the classification results and data mining.

Abstract In current research on reversible visible watermarking algorithm, the original visible watermark image plays an important auxiliary role, and some algorithms also entirely depend on it to restore host image without any distortion. Therefore, in order to realize semi-blind reversible visible watermarking algorithm, the conventional reversible watermarking algorithm is used to embed compressed visible watermark image data into non-visible-watermarked region of host image. However, the amount of compressed image data obtained by conventional image compression algorithm is relatively large. Therefore, a method based on vectorization compression for the visible watermark image is proposed in this paper. Firstly, it performs edge detection on visible watermark image to obtain a discrete points set $\Gamma $ of vector contour curve. Then, the discrete points in $\Gamma $ are simplified by improved Douglas–Peucker algorithm, after that it obtains compressed vector contour data of visible watermark image. In addition, a reversible visible watermarking algorithm based on convolutional relief and image alpha fusion is proposed, which realizes reversible embedding of visible watermark image and lossless restoration of host image. The experimental results show that the proposed vectorization compression method has more advantages than traditional image compression algorithms, which greatly reduces the storage space of visible watermark image with high fidelity. Additionally, the embedded watermarking image has translucent 3D relief effect, and the fusion of host image and visible watermark image becomes more natural and harmonious.

Reversible visible watermarking (RVW) is an active copyright protection mechanism. It not only transparently superimposes copyright patterns on specific positions of digital images or video frames to declare the copyright ownership information, but also completely erases the visible watermark image and thus enables restoring the original host image without any distortion. However, existing RVW algorithms mostly construct the reversible mapping mechanism for a specific visible watermarking scheme, which is not versatile. Hence, we propose a generic RVW framework to accommodate various visible watermarking schemes. In particular, we obtain a reconstruction data packet-the compressed difference image between the watermarked image and the original host image, which is embedded into the watermarked image via any conventional reversible data hiding method to facilitate the blind recovery of the host image. The key is to achieve compact compression of the difference image for efficient embedding of the reconstruction data packet. To this end, we propose regularized Graph Fourier Transform (GFT) coding, where the difference image is smoothed via the graph Laplacian regularizer for more efficient compression and then encoded by multi-resolution GFTs in an approximately optimal manner. Experimental results show that the proposed framework has much better versatility than state-of-the-art methods. Due to the small amount of auxiliary information to be embedded, the visual quality of the watermarked image is also higher.

Aiming at the problem that the robustness, visibility, and transparency of the existing visible watermarking technologies are difficult to achieve a balance, this paper proposes an adaptive embedding method for visible watermarking. Firstly, the salient region of the host image is detected based on superpixel detection. Secondly, the flat region with relatively low complexity is selected as the embedding region in the nonsalient region of the host image. Then, the watermarking strength is adaptively calculated by considering the gray distribution and image texture complexity of the embedding region. Finally, the visible watermark image is adaptively embedded into the host image with slight adjustment by just noticeable difference (JND) coefficient. The experimental results show that our proposed method improves the robustness of visible watermarking technology and greatly reduces the risk of malicious removal of visible watermark image. Meanwhile, a good balance between the visibility and transparency of the visible watermark image is achieved, which has the advantages of high security and ideal visual effect.

Recently, a method based on multiple histograms modification (MHM) is proposed for reversible data hiding (RDH), in which a sequence of prediction-error histograms are generated and two expansion bins are selected in each histogram for expansion embedding. However, although efficient, it only chooses a single pair of expansion bins which limits the embedding capacity. On the other hand, the exhaustive expansion-bin-selection procedure in MHM takes huge computation time, so that it cannot be extended for high capacity RDH. In order to overcome the aforementioned drawbacks, an optimal RDH scheme based on MHM for high capacity embedding is proposed in this paper. First, to improve the embedding capacity, instead of a single pair of expansion bins, multiple pairs of expansion bins are utilized for each histogram, and the multiple-expansion-bin-selection for optimal embedding is formulated as an optimization problem. Then, unlike the exhaustive searching way used in MHM, a computationally efficient algorithm is proposed to solve the optimization problem, so that the optimal expansion bins can be adaptively determined to optimize the embedding performance. By the proposed approach, high embedding capacity can be achieved with good marked image quality, and the experimental results show that it is better than the original MHM and some other state-of-the-art methods.

Pixel-value-ordering (PVO) is an efficient technique of reversible data hiding (RDH). By PVO, the maximum and minimum in each cover image block are first predicted and then modified to embed data. Actually, many PVO-based methods are essentially based on high-dimensional histogram modification. For these methods, a two-dimensional (2D) prediction-error histogram (PEH) is first generated and then modified based on a 2D mapping. However, these methods have two drawbacks. On one hand, the generated 2D PEH is irregular so that it is difficult to design suitable histogram modification strategy. On the other hand, the employed 2D mapping is empirically designed, and thus the embedding performance is far from optimal. Based on these considerations, a new PVO-based RDH scheme is proposed in this paper. By considering both pixel value orders and pixel locations, a new predictor is proposed so that the generated 2D PEH is regular in shape and suitable for reversible embedding. Moreover, instead of manually designing 2D mappings, to optimize the embedding performance, a self-learning mechanism is proposed to adaptively select the 2D mapping according to the image content. With the new predictor and the self-learning mechanism for 2D mapping selection, the proposed method works well with a good marked image quality, e.g., the PSNR of the image Lena is as high as 61.53 dB for an embedding capacity of 10 000 bits. Besides, compared with some state-of-the-art RDH methods, the superiority of the proposed method is experimentally verified.

Local binary pattern (LBP) is an effective image texture descriptor due to its high discrimination and easy computation. Moreover, as an extension, multi-scale LBP (MS-LBP) has also been explored for enhancing the conventional LBP and its miscellaneous variants by combining local image structures of different scales. However, since LBPs of different scales are simply combined in a concatenate or joint way, the cross-scale correlation is not fully utilized in MS-LBP. Based on this thought, we propose in this paper a new LBP variant named path integral based LBP (pi-LBP). Specifically, unlike MS-LBP which encodes local patterns individually in each scale, the different scales pixels along a specific path are filtered and then encoded in pi-LBP. In this way, by taking different paths and filters, pi-LBP can effectively encode the cross-scale correlation and provide a better texture description. Experimental results on Outex texture suites show that the proposed pi-LBP outperforms MS-LBP and some other LBP variants on texture classification.