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This paper explores a new framework for lossy image encryption and decryptionusing a simple shallow encoder neural network E for encryption, and a complexdeep decoder neural network D for decryption. E is kept simple so that encodingcan be done on low power and portable devices and can in principle be anynonlinear function which outputs an encoded vector. D is trained to decode theencodings using the dataset of image - encoded vector pairs obtained from E andhappens independently of E. As the encodings come from E which while being asimple neural network, still has thousands of random parameters and thereforethe encodings would be practically impossible to crack without D. This approachdiffers from autoencoders as D is trained completely independently of E,although the structure may seem similar. Therefore, this paper also exploresempirically if a deep neural network can learn to reconstruct the original datain any useful form given the output of a neural network or any other nonlinearfunction, which can have very useful applications in Cryptanalysis. Experimentsdemonstrate the potential of the framework through qualitative and quantitativeevaluation of the decoded images from D along with some limitations.

Shallow Encoder Deep Decoder (SEDD) Networks for Image Encryption and Decryption