Efficient Reversible Data Hiding Using Two-Dimensional Pixel Clustering

Abstract

Pixel clustering is a technique of content-adaptive data embedding in the area of high-performance reversible data hiding (RDH). Using pixel clustering, the pixels in a cover image can be classified into different groups based on a single factor, which is usually the local complexity. Since finer pixel clustering seems to improve the embedding performance, in this manuscript, we propose using two factors for two-dimensional pixel clustering to develop high-performance RDH. Firstly, in addition to the local complexity, a novel factor was designed as the second factor for pixel clustering. Specifically, the proposed factor was defined using the rotation-invariant code derived from pixel relationships in the four-neighborhood. Then, pixels were allocated to the two-dimensional clusters based on the two clustering factors, and cluster-based pixel prediction was realized. As a result, two-dimensional prediction-error histograms (2D-PEHs) were constructed, and performance optimization was based on the selection of expansion bins from the 2D-PEHs. Next, an algorithm for fast expansion-bin selection was introduced to reduce the time complexity. Lastly, data embedding was realized using the technique of prediction-error expansion according to the optimally selected expansion bins. Extensive experiments show that the embedding performance was significantly enhanced, particularly in terms of improved image quality and reduced time complexity, and embedding capacity also moderately improved.

Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain

“…In this case, we can directly extract the watermark information from the distorted image I a for copyright verification. We adopted a generic reversible algorithm framework and traditional reversible watermarking methods such as histogram modification (HM) [24], pixel value ordering (PVO) [25], difference expansion (DE) [26], and prediction error expansion (PEE) [27] techniques. In this paper, we specifically used difference expansion.…”

Adaptive 3D Reversible Data Hiding Technique Based on the Cumulative Peak Bins in the Histogram of Directional Prediction Error

“…The size of the hidden message must be balanced to ensure the quality of the technique. To meet these requirements, researchers have proposed various RDH techniques, including cryptographic-based, perception-based, pixel clustering, LSB-based, histogram-based, and feature extraction-based [6,[9][10][11][12]. RDH based on the histogram has limitations, including susceptibility to statistical attacks such as histogram analysis and a relatively small embedding capacity compared to other methods; thus, increasing embedding capacity reduces image quality and requires a location map that increases the amount of information to be embedded.…”

“…Although most existing RDH methods can accurately extract the embedded information, the embedding process causes noticeable distortion to the carrier image. An attacker can identify any irregularities or inconsistencies in the image histogram and quality distortions, which would give away the presence of hidden information [12][13][14][15][16][17]. Furthermore, traditional histogram shifting RDH techniques [11,12,[18][19][20][21] shift the peak points to the zero point of the image histogram while ignoring the amount of invalid shifting pixels (ISPs), resulting in redundant locations that cause significant image distortion; the pixels are shifted but not used in the embedding process, making it more vulnerable to attacks.…”

“…An attacker can identify any irregularities or inconsistencies in the image histogram and quality distortions, which would give away the presence of hidden information [12][13][14][15][16][17]. Furthermore, traditional histogram shifting RDH techniques [11,12,[18][19][20][21] shift the peak points to the zero point of the image histogram while ignoring the amount of invalid shifting pixels (ISPs), resulting in redundant locations that cause significant image distortion; the pixels are shifted but not used in the embedding process, making it more vulnerable to attacks. Usually, the prediction error histogram is used in conjunction with histogram modification to create prediction-error histograms (PEHs), from which the appropriate expansion bins for histogram shifting and histogram expansion are chosen.…”

High-Capacity Imperceptible Data Hiding Using Permutation-Based Embedding Process for IoT Security

“…Depending on the applications' requirements, reversible ones recover the original media, while irreversible ones either do not care about the original media or the stego media has a comparable quality to its originals. Moreover, the data hiding methods can be classified into four categories according to the domains being processed: spatial [11][12][13][14][15][16][17][18], transform [19][20][21], compressed [22][23][24], and encryption [8,[25][26][27]. When using spatial domain methods, secret data are embedded into cover images by modifying their pixel values.…”