Abstract

This paper presents a Reversible Data Hiding method in grayscale digital images using Pixel Prediction Error. The prediction error of the prediction error is modified to store the secret data. The prediction of the image pixels is carried out using interpolation from neighboring pixels. The high spatial correlation of image pixels in natural images lead to prediction errors close to zero. In the second step a further interpolation of prediction errors are applied to get the prediction error of prediction errors. These errors are then modified by using histogram modification procedure to carry the secret data in binary form. A novel artificial neural networks based system is trained to find the optimal sorting order of the prediction errors for embedding. In the existing work, a simple local complexity measure is used as a proxy for pixel prediction errors. However, in the proposed work the neural networks based solution gives a more optimal order to embed pixels. Experimental results and analysis are carried out with a set of eight test images with varying characteristics. It is shown that the proposed pixel sorting method gives better visual performance for the same embedding rate compared against existing procedure. The average Peak Signal to Noise Ratio of the proposed work is 56.1 dB which is better than 54.40 dB given by existing work.

中文翻译：

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基于神经网络的排序顺序隐藏在像素预测误差中的可逆数据

摘要

本文提出了一种利用像素预测误差的灰度数字图像可逆数据隐藏方法。修改预测误差的预测误差以存储秘密数据。使用来自相邻像素的插值来进行图像像素的预测。自然图像中图像像素的高空间相关性导致预测误差接近零。在第二步骤中，应用预测误差的进一步内插以获得预测误差的预测误差。然后，通过使用直方图修改过程来修改这些错误，以二进制形式携带秘密数据。训练基于新颖的人工神经网络的系统，以找到用于嵌入的预测误差的最佳排序顺序。在现有工作中，一个简单的局部复杂性度量用作像素预测误差的代理。然而，在提出的工作中，基于神经网络的解决方案给出了嵌入像素的最佳顺序。实验结果和分析使用一组八张具有不同特征的测试图像进​​行。结果表明，与现有程序相比，所提出的像素排序方法在相同的嵌入率下具有更好的视觉性能。拟议工作的平均峰值信噪比为56.1 dB，优于现有工作给出的54.40 dB。结果表明，与现有程序相比，所提出的像素排序方法在相同的嵌入率下具有更好的视觉性能。拟议工作的平均峰值信噪比为56.1 dB，优于现有工作给出的54.40 dB。结果表明，与现有程序相比，所提出的像素排序方法在相同的嵌入率下具有更好的视觉性能。拟议工作的平均峰值信噪比为56.1 dB，优于现有工作给出的54.40 dB。