  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.
