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Aiming at solving the problems of bad imperceptibility and low embedding rate of existing algorithms, a novel large-capacity reversible image watermarking based on improved difference expansion (DE) is proposed. Firstly, the smoothness calculation algorithm is used to calculate and sort the smoothness values of the divided image sub-blocks; then, the scrambled watermark is embedded into the sub-blocks with less smoothness after removing the abrupt point by using the generalized difference expansion (GDE); finally, the absolute difference operation is applied to the generated overflow pixels to make their pixel values within a reasonable range for embedding watermark information. Under the premise of ensuring a certain visual quality, multiple watermark embedding can effectively improve the embedding rate. The simulation results show that this algorithm not only realizes blind extraction, but also recovers the original images without loss. At the same time, this algorithm achieves a high embedding rate (the average embedding rate is as high as 77.91 dB) without decreasing the visual quality.

Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. This is because placing data in an image can cause permanent distortion after data mining. Therefore, a reversible data hiding (RDH) technique is required. One of the well-known designs of RDH is the difference expansion (DE) method. In the DE-based RDH method, finding spaces that create less distortion in the marked image is a significant challenge, and has a high insertion capacity. Therefore, the smaller the difference between the selected pixels and the more correlation between two consecutive pixels, the less distortion can be achieved in the image after embedding the secret data. This paper proposes a multilayer RDH method using the multilevel thresholding technique to reduce the difference value in pixels and increase the visual quality and the embedding capacity. Optimization algorithms are one of the most popular methods for solving NP-hard problems. The slime mould algorithm (SMA) gives good results in finding the best solutions to optimization problems. In the proposed method, the SMA is applied to the host image for optimal multilevel thresholding of the image pixels. Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. Experimental results show that the proposed method is better than comparable methods regarding the degree of distortion, quality of the marked image, and insertion capacity. [ABSTRACT FROM AUTHOR]

In recent years, many reversible information hiding methods have been proposed. Among them, the pixel value ordering (PVO) method can be used to create high-fidelity camouflage images under good embedding capacity. The original PVO method adopted the block-by-block manner, and each block could embed only 2 bits. In this study, we propose an overlapping PVO (OPVO) method. In the PPVO method, secret data are embedded in the maximum and minimum pixel values to improve the situation in which only up to 1 bit can be embedded in a sliding window. In the pixel-by-pixel manner, each pixel can embed data multiple times. We make full use of the correlation between adjacent pixels in the natural image to increase the embedding capacity. An advantage of the overlapping PVO method is that its embedding capacity is more than twice as high as that of previous PVO series methods where an acceptable image quality is maintained. In our experiment, the embedding capacity of the smooth image is up to 130,000 bits, whereas that of the complex image is higher. However, the image quality decrease rate of the proposed method tends to be lower than that of the other methods. Therefore, any kind of image is suitable for our method. [ABSTRACT FROM AUTHOR]

In order to improve the embedding rate of reversible watermarking algorithm for digital image and enhance the imperceptibility of the watermarked image, an adaptive reversible image watermarking algorithm based on DE is proposed. By analyzing the traditional DE algorithm and the generalized DE algorithm, an improved difference expansion algorithm is proposed. Through the analysis of image texture features, the improved algorithm is used for embedding and extracting the watermark. At the same time, in order to improve the embedding capacity and visual quality, the improved algorithm is optimized in this paper. Simulation results show that the proposed algorithm can not only achieve the blind extraction, but also significantly heighten the embedded capacity and non-perception. Moreover, compared with similar algorithms, it is easy to implement, and the quality of the watermarked images is high. [ABSTRACT FROM AUTHOR]

It is important to restore original image and watermark in many applications such as military and medical. It is also referred to as 'reversible watermarking'. Individual algorithm is difficult to analyze for a particular application, hence we compare the algorithm on some criteria. Here we present a comparative study of Difference Expansion (DE), Reversible Contrast Mapping (RCM) and Histogram Shifting (HS) methods. The results showed that HS method is the best method. It gave better PSNR than the other two methods, while embedding capacity of DE method proved to be better than the other two methods. We tested all methods for high and low quality images and found that all methods are suitable only for high quality images, while HS method is additionally suitable for cartoon and medical images. DE and RCM methods need improvement for applications in cartoon and medical images. [ABSTRACT FROM AUTHOR]

In reversible data hiding (RDH), the original cover can be losslessly restored after the embedded information is extracted. Kalker and Willems established a rate–distortion model for RDH, in which they proved out the rate–distortion bound and proposed a recursive code construction. In our previous paper, we improved the recursive construction to approach the rate–distortion bound. In this paper, we generalize the method in our previous paper using a decompression algorithm as the coding scheme for embedding data and prove that the generalized codes can reach the rate–distortion bound as long as the compression algorithm reaches entropy. By the proposed binary codes, we improve three RDH schemes that use binary feature sequence as covers, i.e., an RS scheme for spatial images, one scheme for JPEG images, and a pattern substitution scheme for binary images. The experimental results show that the novel codes can significantly reduce the embedding distortion. Furthermore, by modifying the histogram shift (HS) manner, we also apply this coding method to one scheme that uses HS, showing that the proposed codes can be also exploited to improve integer-operation-based schemes. [ABSTRACT FROM AUTHOR]

Reversible watermarking enables the embedding of useful information in a host signal without any loss of host information. Tian's difference-expansion technique is a high-capacity, reversible method for data embedding However, the method suffers from undesirable distortion at low embedding capacities and lack of capacity control due to the need for embedding a location map. We propose a histogram shifting technique as an alternative to embedding the location map. The proposed technique improves the distortion performance at low embedding capacities and mitigates the capacity control problem. We also propose a reversible data-embedding technique called prediction-error expansion. This new technique better exploits the correlation inherent in the neighborhood of a pixel than the difference-expansion scheme. Prediction-error expansion and histogram shifting combine to form an effective method for data embedding. The experimental results for many standard test images show that prediction-error expansion doubles the maximum embedding capacity when compared to difference expansion. There is also a significant improvement in the quality of the watermarked image, especially at moderate embedding capacities. [ABSTRACT FROM AUTHOR]

Aiming at solving the problems of bad imperceptibility and low embedding rate of existing algorithms, a novel large-capacity reversible image watermarking based on improved difference expansion (DE) is proposed. Firstly, the smoothness calculation algorithm is used to calculate and sort the smoothness values of the divided image sub-blocks; then, the scrambled watermark is embedded into the sub-blocks with less smoothness after removing the abrupt point by using the generalized difference expansion (GDE); finally, the absolute difference operation is applied to the generated overflow pixels to make their pixel values within a reasonable range for embedding watermark information. Under the premise of ensuring a certain visual quality, multiple watermark embedding can effectively improve the embedding rate. The simulation results show that this algorithm not only realizes blind extraction, but also recovers the original images without loss. At the same time, this algorithm achieves a high embedding rate (the average embedding rate is as high as 77.91 dB) without decreasing the visual quality.