基于细胞非线性网络和局部图像描述子的鲁棒图像密码方案
A robust image-based cryptology scheme based on cellular non-linear network and local image descriptors
摘要：
元胞非线性网络（CNN）为元胞自动机提供了一个基础设施，使其不仅具有初始状态，而且具有在每个元胞中具有局部记忆的输入，其复杂度要高得多。这个属性有许多应用，我们已经研究了它在提出一个强大的密码方案。该方案由密码学和隐写子模块组成，其中设计了一个三维CNN，产生一个混沌映射作为系统的核心，以保持密码学中的机密性和数据完整性。我们的贡献是三方面的，包括（1）一个特征描述符被应用到封面图像，而传统的方法使用一个预定义的密钥，（2）一个三维CNN是用来制作一个混沌映射，从视觉信息的密码，和（3）所提出的CNN也被用来制作一个动态的$k$ -LSB隐写。在25幅标准图像上进行的实验证明了该方案在安全性、视觉效果和复杂度分析方面的有效性。
英文摘要：
Cellular nonlinear network (CNN) provides an infrastructure for Cellular Automata to have not only an initial state but an input which has a local memory in each cell with much more complexity. This property has many applications which we have investigated it in proposing a robust cryptology scheme. This scheme consists of a cryptography and steganography sub-module in which a 3D CNN is designed to produce a chaotic map as the kernel of the system to preserve confidentiality and data integrity in cryptology. Our contributions are three-fold including (1) a feature descriptor is applied to the cover image to form the secret key while conventional methods use a predefined key, (2) a 3D CNN is used to make a chaotic map for making cipher from the visual message, and (3) the proposed CNN is also used to make a dynamic $k$-LSB steganography. Conducted experiments on 25 standard images prove the effectiveness of the proposed cryptology scheme in terms of security, visual, and complexity analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
