A robust image-based cryptology scheme based on cellular non-linear network and local image descriptors
This work addresses secure image transmission for applications like digital media, but it is incremental as it builds on existing cellular nonlinear network and local descriptor methods.
The paper tackles the problem of secure image communication by proposing a robust cryptology scheme using a 3D cellular nonlinear network to generate chaotic maps for encryption and steganography, with experiments on 25 standard images demonstrating effectiveness in security, visual quality, and complexity.
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.