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Quantization selection based on characteristic of cover image for PVD Steganography to optimize imperceptibility and capacity

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Abstract

Pixel Value Differencing (PVD) is one of the popular steganography methods and continues to be developed today. This method works based on differences in pixel values ​​in the image. Each cover image has a different embedding capacity based on the difference in pixel values ​​and their quantification range. PVD is superior in the aspects of capacity and imperceptibility based on the human visual system (HVS), which is widely measured using a structural similarity index (SSIM). By default, PVD has two quantization ranges. After further evaluation using SSIM in several types of cover images, the results do not always match due to differences in the characteristics of the cover image. PVD is mostly developed with adaptive quantization, which directly calculates the pixel value to optimize the trade-off of capacity and imperceptibility. This research proposes a different method to optimize this trade-off. The method is to learn the cover image characteristics with features extraction and machine learning (ML). This is done in the preprocessing before the embedding process to determine the ideal quantization for each cover image. The proposed method has been tested on the standard PVD method. It is proven to classify the cover image well and improve the quality of the stego image because the selected quantization range is more by the image characteristics. This method can later be developed and combined with the further PVD steganography method or other methods because the process is not integrated with the embedding process.

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Authors and Affiliations

  1. Department of Informatics Engineering, Dian Nuswantoro University, 50131, Semarang, Indonesia

    Pulung Nurtantio Andono & De Rosal Ignatius Moses Setiadi

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  1. Pulung Nurtantio Andono
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  2. De Rosal Ignatius Moses Setiadi
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Correspondence to Pulung Nurtantio Andono.

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Appendix 1

Appendix 1

The following appendix lists abbreviations with their descriptions and/or terminology.

BER:

Bit Error Ratio (image steganography measuring tool to determine the quality of the extraction results)

BPP:

Bit Per Pixel (image steganography measuring tool for payload capacities aspects)

CER:

Bit Error Ratio (image steganography measuring tool to determine the quality of the extraction results)

EMD:

Exploiting Modification Direction (one of the steganographic methods in the spatial domain)

GLCM:

Gray level co-occurrence matrix (one of feature extraction in the image)

HVS:

Human Visual System

KNN:

k-Nearest Neighbor (one of the classification methods in data mining, in this case, image classification)

LSB:

Least Significant Bit (one of the steganographic methods in the spatial domain)

ML:

Machine Learning (data mining method)

NB:

Naïve Bayes (one of the classification methods in data mining)

PSNR:

peak signal-to-noise ratio (image steganography measuring tool for imperceptibility aspects)

PVD:

Pixel value Differencing (one of the steganographic methods in the spatial domain)

RS:

Regular Singular (image steganography measuring tool for security aspects)

SSIM:

structural similarity index (image steganography measuring tool for imperceptibility aspects)

SVM:

Support Vector Machine

Q1:

PVD quantization range ([8 8 16 32 64 128] excels in payload capacity)

Q2:

PVD quantization range ([2 2 4 4 8 8 16 16 32 32 64 64] excels in imperceptibility)

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Andono, P.N., Setiadi, D.R.I.M. Quantization selection based on characteristic of cover image for PVD Steganography to optimize imperceptibility and capacity. Multimed Tools Appl 82, 3561–3580 (2023). https://doi.org/10.1007/s11042-022-13393-y

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