In the reversible data hiding (RDH) community, both prediction and mapping strategies are vital for reducing distortion. With high prediction performance, small prediction errors can be generated to reduce the embedding distortion. Besides, the efficient mapping strategy can improve the practicality. In this paper, we propose a new RDH method for color images by using convolution neural networks (CNNs) for prediction and an efficient 3D mapping strategy for embedding. At first, each color image is elaborately divided into three isolated image sets so that the proposed deep prediction network (DPN) can exploit more neighboring pixels in the current channel and the correlation between three channels. Then, an efficient 3D mapping strategy is luminously designed by using the symmetry of the 3D prediction error histogram (PEH). The symmetry of 3D PEH has been analyzed in statistical and experimental ways. Based on the proposed deep prediction network and efficient 3D mapping strategy (DPEM), we construct an efficient RDH method for color images. The performance of the proposed DPN is evaluated by comparing it with several predictors on different image datasets. The embedding performance has been demonstrated by hiding information in color images, e.g., the average PSNR value of the Kodak dataset is 63.63 dB with an embedding capacity of 50,000 bits. Furthermore, the experimental results on the ImageNet and PASCAL VOC2012 datasets have shown the proposed RDH method is superior to several state-of-the-art RDH methods. With the introduction of deep learning, the development of the RDH method for color images can be promoted.

Pixel-value-ordering (PVO) is one of the most popular methods in reversible data hiding (RDH). In PVO based methods, pixels are processed in a block-wise way, so that the local similarities of the images are considered but the global statistical characteristics are ignored. To better utilize the correlations of pixels, this paper proposes a global sorting strategy to combine utilizations of local and global characteristics of the images. For each pixel, its prediction value and local complexity are first calculated based on its local characteristics. Then the image pixels are sorted globally according to their prediction values to generate a single-sorted pixel sequence, in which the pixels with the equal prediction values are sorted again by referring to their local complexities. In such a way, the spatial distances of image pixels are broken so that the global statistical characteristics can be well exploited. With the proposed sorting strategy, we can obtain a more regular 2D histogram by segmenting the sorted sequence for the location-based PVO (LPVO) predictor. Owe to the regular 2D histogram, we have designed an efficient 2D mapping to achieve perfect performance for all the tested images. With the proposed RDH scheme, the PSNR of the image Lena is as high as 61.86 dB and the average PSNR of the Kodak dataset reaches 63.55 dB after embedding 10,000 bits. The superiority of the proposed method has been verified by comparing with recent state-of-the-art RDH methods.

In the field of invertible image decolorization, how to reduce artifacts in the smooth grayscale regions and prevent color distortion at the boundaries of the reconstructed color image is a crucial issue. In this paper, we propose an invertible deep learning network with extraction and hiding of color information. Our approach separates the original color image into the luminance and chromaticity planes by using orthogonal transformation, which enhances the independence and completeness of color and luminance information. Then, the color feature extraction module is developed to minimize color information distortion, while the color hiding module is adopted to hide color information invisibly. Compared with existing deep-learning-based methods, the proposed network can preserve more color information while ensuring the quality of grayscale images by processing color and grayscale information separately. Furthermore, we propose a reversible data hiding strategy that enhances the performance of the reconstructed color images. Our method outperforms learned invertible image decolorization methods, as demonstrated through experiments on the VOC2012, Kodak24, and NCD datasets.

After the prediction, how to modify the 2D prediction error histogram (PEH) for reversible data hiding (RDH) is an important issue. In this letter, we propose an efficient 2D mapping strategy by considering the error pairs on two diagonal lines of the 2D PEH. The proposed 2D mapping can quickly adapt to the frequencies of the error pairs for different mapping area sizes. As a bonus, a satisfactory embedding performance can be achieved since in each group of two symmetrical error pairs the one with larger frequencies has priority to select more mapping ways. Experimental results have shown that it is low time-consuming and achieves better embedding performance in comparison with several classical and advanced 2D mappings.

In the field of reversible data hiding (RDH), how to predict an image and embed a message into the image with smaller distortion are two important aspects. In this paper, we propose a novel and efficient RDH method by innovating an intelligent predictor and an adaptive embedding way. In the prediction stage, we first constructed a convolutional neural network (CNN) based predictor by reasonably dividing an image into four parts. In such a way, each part can be predicted by using the other three parts as the context for the improvement of the prediction performance. Compared with existing predictors, the proposed CNN predictor can use more neighboring pixels for the prediction by exploiting its multi-receptive fields and global optimization capacities. In the embedding stage, we also developed a prediction-error-ordering (PEO) based adaptive embedding strategy, which can better adapt image content and thus efficiently reduce the embedding distortion by elaborately and luminously applying background complexity to select and pair those smaller prediction errors for data hiding. With the proposed CNN prediction and embedding ways, the RDH method presented in this paper provides satisfactory results in improving the visual quality of data hidden images, e.g., the average PSNR value for the Kodak benchmark dataset can reach as high as 63.59 dB with an embedding capacity of 10,000 bits. Extensive experimental results have shown that the RDH method proposed in this paper is superior to those existing state-of-the-art works.

Invertible color-to-grayscale conversion is a method that embeds the color information into the corresponding grayscale image and extracts the color information to reconstruct the color image when necessary. In this paper, we propose an efficient method by using K-means clustering to generate a color palette and its corresponding grayscale image, and further making use of a reversible watermarking technique to embed the color palette into the grayscale image. For purpose of integrality authentication, the grayscale image is hashed as part of the embedded information before the embedding. In the process of reconstructing the color image, the color palette can be extracted correctly and the grayscale image can be recovered without any loss. Experimental results have shown that the proposed method can provide satisfactory performance.

How to predict images is an important issue in the reversible data hiding (RDH) community. In this letter, we propose a novel CNN-based prediction approach by luminously dividing a grayscale image into two sets and applying one set to predict the other set for data embedding. The proposed CNN predictor is a lightweight and computation-efficient network with the capabilities of multi receptive fields and global optimization. This CNN predictor can be trained quickly and well by using 1000 images randomly selected from ImageNet. Furthermore, we propose a two stages of embedding scheme for this predictor. Experimental results show that the CNN predictor can make full use of more surrounding pixels to promote the prediction performance. Furthermore, in the experimental way we have shown that the CNN predictor with expansion embedding and histogram shifting techniques can provide better embedding performance in comparison with those classical linear predictors.

Cover-lossless robust watermarking is a new research issue in the information hiding community, which can restore the cover image completely in case of no attacks. Most countermeasures proposed in the literature usually focus on additive noise-like manipulations such as JPEG compression, low-pass filtering and Gaussian additive noise, but few are resistant to challenging geometric deformations such as rotation and scaling. The main reason is that in the existing cover-lossless robust watermarking algorithms, those exploited robust features are related to the pixel position. In this article, we present a new cover-lossless robust image watermarking method by efficiently embedding a watermark into low-order Zernike moments and reversibly hiding the distortion due to the robust watermark as the compensation information for restoration of the cover image. The amplitude of the exploited low-order Zernike moments are: 1) mathematically invariant to scaling the size of an image and rotation with any angle; and 2) robust to interpolation errors during geometric transformations, and those common image processing operations. To reduce the compensation information, the robust watermarking process is elaborately and luminously designed by using the quantized error, the watermarked error and the rounded error to represent the difference between the original and the robust watermarked image. As a result, a cover-lossless robust watermarking system against geometric deformations is achieved with good performance. Experimental results show that the proposed robust watermarking method can effectively reduce the compensation information, and the new cover-lossless robust watermarking system provides strong robustness to those content-preserving manipulations including scaling, rotation, JPEG compression and other noise-like manipulations. In case of no attacks, the cover image can be recovered without any loss.