High-Embedded Low-Distortion Multihistogram Shift Video Reversible Data Hiding Based on DCT Coefficient

Abstract

Video reversible data hiding technology can be applied to copyright protection, medical images, the military, and other fields, but it cannot guarantee high visual quality with an effective embedded capacity. In this paper, a high-embedding and low-distortion reversible data hiding scheme based on a discrete cosine transform (DCT) coefficients method is proposed. The scheme first decodes the original video stream with entropy, obtains all the DCT blocks, and selects the embeddable DCT blocks according to the capacity of the zero factor. Then, it divides the coefficients in the DCT blocks into the shift and embedding coefficients. The shift coefficients directly generate a one-dimensional histogram; the embedding coefficients generate a two-dimensional histogram according to paired strategies. Finally, the secret data can be successfully embedded according to the proposed two-dimensional histogram shift reversible data hiding scheme. This scheme performed more effectively than existing schemes in terms of the embedded capacity, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM).

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.…”

“…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.…”