Image Generation Method Based on the Recoverable Byte Sequence using the Neural Networks

Q3 Mathematics
I. V. Rudakov, M. V. Filippov, M. A. Kudryavtsev
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引用次数: 0

Abstract

Due to the rapid development of information technologies, the tasks of ensuring information integrity, safety and confidentiality, as well as the possibility of guaranteed confirmation of its source, are becoming more relevant than ever. One of the possible solutions to this problem could be the steganographic methods, which allow both hiding the fact of information transfer and imperceptibly adding the useful data. Scientific literature describes a large number of steganographic algorithms. However, only insignificant number of works is devoted to the data hiding methods using the neural networks, and even less are devoted to generating containers for them. A method for generating images based on the hidden information is proposed, which guarantees possibility of both hiding and subsequent extraction of information eliminating the need to select an appropriate container. As part of the method, an algorithm was developed that included description of the stages of input data preprocessing, transformation into the container image and extraction of the hidden information. Examples of the proposed method operation are provided. The method could serve both as a steganographic algorithm for hiding information and as the algorithm for adding information in the form of watermarks
基于可恢复字节序列的神经网络图像生成方法
由于信息技术的迅速发展,确保信息的完整性、安全性和保密性以及保证确认其来源的可能性的任务变得比以往任何时候都更加重要。这个问题的一个可能的解决方案是隐写方法,它既可以隐藏信息传输的事实,又可以在不知不觉中添加有用的数据。科学文献描述了大量的隐写算法。然而,研究神经网络数据隐藏方法的文献并不多,研究神经网络容器生成的文献就更少了。提出了一种基于隐藏信息生成图像的方法,保证了信息的隐藏和后续提取的可能性,省去了选择合适容器的需要。作为该方法的一部分,开发了一种算法,该算法包括输入数据预处理、转换到容器图像和提取隐藏信息的阶段描述。给出了所提方法操作的实例。该方法既可以作为隐藏信息的隐写算法,也可以作为以水印形式添加信息的算法
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
40
期刊介绍: The journal is aimed at publishing most significant results of fundamental and applied studies and developments performed at research and industrial institutions in the following trends (ASJC code): 2600 Mathematics 2200 Engineering 3100 Physics and Astronomy 1600 Chemistry 1700 Computer Science.
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