Chen Yuhan, Sano Hideki, Wakaiki Masashi, Yaguchi Takaharu
Graduate School of System Informatics, Kobe University, Kobe 657-8501, Japan.
Entropy (Basel). 2021 Jul 16;23(7):904. doi: 10.3390/e23070904.
In a secret communication system using chaotic synchronization, the communication information is embedded in a signal that behaves as chaos and is sent to the receiver to retrieve the information. In a previous study, a chaotic synchronous system was developed by integrating the wave equation with the van der Pol boundary condition, of which the number of the parameters are only three, which is not enough for security. In this study, we replace the nonlinear boundary condition with an artificial neural network, thereby making the transmitted information difficult to leak. The neural network is divided into two parts; the first half is used as the left boundary condition of the wave equation and the second half is used as that on the right boundary, thus replacing the original nonlinear boundary condition. We also show the results for both monochrome and color images and evaluate the security performance. In particular, it is shown that the encrypted images are almost identical regardless of the input images. The learning performance of the neural network is also investigated. The calculated Lyapunov exponent shows that the learned neural network causes some chaotic vibration effect. The information in the original image is completely invisible when viewed through the image obtained after being concealed by the proposed system. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method. Some security tests are also performed. The proposed method is designed in such a way that the transmitted images are encrypted into almost identical images of waves, thereby preventing the retrieval of information from the original image. The numerical results show that the encrypted images are certainly almost identical, which supports the security of the proposed method.
在使用混沌同步的秘密通信系统中，通信信息被嵌入到一个表现为混沌的信号中，并发送给接收方以检索信息。在先前的一项研究中，通过将波动方程与范德波尔边界条件相结合开发了一种混沌同步系统，其参数数量仅为三个，安全性不足。在本研究中，我们用人工神经网络取代非线性边界条件，从而使传输的信息难以泄露。神经网络分为两部分；前半部分用作波动方程的左边界条件，后半部分用作右边界条件，从而取代了原来的非线性边界条件。我们还展示了单色和彩色图像的结果并评估了安全性能。特别地，结果表明无论输入图像如何，加密后的图像几乎都是相同的。我们还研究了神经网络的学习性能。计算得到的李雅普诺夫指数表明，经过学习的神经网络会产生一些混沌振动效应。通过所提出的系统进行隐藏后得到的图像来看，原始图像中的信息完全不可见。我们还进行了一些安全性测试。所提出的方法设计成将传输的图像加密成几乎相同的波形图像，从而防止从原始图像中检索信息。数值结果表明加密后的图像确实几乎相同，这支持了所提出方法的安全性。我们还进行了一些安全性测试。所提出的方法设计成将传输的图像加密成几乎相同的波形图像，从而防止从原始图像中检索信息。数值结果表明加密后的图像确实几乎相同，这支持了所提出方法的安全性。
