一种基于自组织映射和HVS的隐写方法

Jiajia Zhang, Hui-xian Huang, Chenhao Wang, Hongbin Pan
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引用次数: 2

摘要

为了提供大容量隐藏的秘密数据和隐写图像的不可感知性,本文提出了一种基于自组织映射和HVS的图像隐写新方法。根据对比度和纹理敏感性,训练基于竞争学习的自组织地图。因此训练好的神经网络是嵌入和提取秘密数据的关键。该方法利用相邻的像素(上、左、右、下)来估计经过训练的神经网络像素的敏感程度,以便在不太敏感的区域的像素可能携带更多隐藏数据。从实验结果来看,与SOC相比,该方法可以隐藏更大的信息,并保持更好的隐写图像视觉质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A steganographic method based on self-organizing map and HVS
In order to provide large capacity of hidden secret data and imperceptibility of stego-image, in this paper, a novel image steganographic method based on self-organizing map and HVS was presented. According to contrast and texture sensitivity, self-organizing map based on competitive learning is trained. So NNs trained is the key of the embedded and extracted secret data. The method exploits neighboring pixels(upper, left, right, bottom) to estimate the degree of sensitivity of pixels with NNs trained so that pixels in less sensitive areas can potentially carry more hidden data. From the experimental results, compared with SOC, the proposed method can hide a much larger information and maintains a better visual quality of stego-image.
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