An auto encoder-decoder based deep convolutional neural network is proposed to embed the secret image inside the cover image and to extract the file There are several image encryption methods currently out there, but few require the automation of the secret key generation process utilizing an unsupervised learning mechanism. In this project, we are attempting to develop a novel encryption system for images using deep auto-encoders, where the pixel values of the images are taken as the initial feature vectors. The project is proposed to be implemented in two ways, to work with grayscale values, as well as RGB values using a convolutional neural network (CNN). The auto-encoder generates a latent space based on the initial features, that while having few visual similarity with the same, serves as a low memory encryption system for the images. The secret key here is the model generated by the auto-encoder, which comprises the architecture and weights of the neural network, and will be possessed beforehand by the end users.
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