  This paper explores a new framework for lossy image encryption and decryption
using a simple shallow encoder neural network E for encryption, and a complex
deep decoder neural network D for decryption. E is kept simple so that encoding
can be done on low power and portable devices and can in principle be any
nonlinear function which outputs an encoded vector. D is trained to decode the
encodings using the dataset of image - encoded vector pairs obtained from E and
happens independently of E. As the encodings come from E which while being a
simple neural network, still has thousands of random parameters and therefore
the encodings would be practically impossible to crack without D. This approach
differs from autoencoders as D is trained completely independently of E,
although the structure may seem similar. Therefore, this paper also explores
empirically if a deep neural network can learn to reconstruct the original data
in any useful form given the output of a neural network or any other nonlinear
function, which can have very useful applications in Cryptanalysis. Experiments
demonstrate the potential of the framework through qualitative and quantitative
evaluation of the decoded images from D along with some limitations.
