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Authenticity Preservation with Histogram-Based Reversible Data Hiding and Quadtree Concepts

机译:基于直方图的可逆数据隐藏和四叉树概念的真实性保护

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摘要

With the widespread use of identification systems, establishing authenticity with sensors has become an important research issue. Among the schemes for making authenticity verification based on information security possible, reversible data hiding has attracted much attention during the past few years. With its characteristics of reversibility, the scheme is required to fulfill the goals from two aspects. On the one hand, at the encoder, the secret information needs to be embedded into the original image by some algorithms, such that the output image will resemble the input one as much as possible. On the other hand, at the decoder, both the secret information and the original image must be correctly extracted and recovered, and they should be identical to their embedding counterparts. Under the requirement of reversibility, for evaluating the performance of the data hiding algorithm, the output image quality, named imperceptibility, and the number of bits for embedding, called capacity, are the two key factors to access the effectiveness of the algorithm. Besides, the size of side information for making decoding possible should also be evaluated. Here we consider using the characteristics of original images for developing our method with better performance. In this paper, we propose an algorithm that has the ability to provide more capacity than conventional algorithms, with similar output image quality after embedding, and comparable side information produced. Simulation results demonstrate the applicability and better performance of our algorithm.
机译:随着识别系统的广泛使用,利用传感器建立真实性已成为重要的研究课题。在使基于信息安全性进行真实性验证的方案中,可逆数据隐藏在过去几年中引起了很多关注。由于具有可逆性的特点,该方案需要从两个方面实现目标。一方面,在编码器上,需要通过一些算法将秘密信息嵌入到原始图像中,以使输出图像尽可能地类似于输入图像。另一方面,在解码器处,必须正确地提取和恢复秘密信息和原始图像,并且它们应该与它们的嵌入副本相同。在可逆性的要求下,为了评估数据隐藏算法的性能,输出图像质量(称为不可感知性)和嵌入位数(称为容量)是访问算法有效性的两个关键因素。此外,还应该评估用于使解码成为可能的辅助信息的大小。在这里,我们考虑使用原始图像的特征来开发性能更好的方法。在本文中,我们提出了一种算法,该算法能够提供比常规算法更大的容量,并且在嵌入后具有相似的输出图像质量,并产生可比较的辅助信息。仿真结果证明了该算法的适用性和更好的性能。

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