Autoencoder-based joint image compression and encryption

Abstract

Recently, learning-based methods have developed rapidly, with promising results in many areas. And deep learning models are expected to be the next-generation optimal image compression solutions. This paper proposes a new image compression and encryption framework that integrates encryption algorithms with a deep-learning compression network. Our work employs Auto-Encoder (AE) based compression network as the backbone. After extracting semantic features from the learning network, encryption layers are added to introduce end-to-end learning to image encryption. And for a higher level of visual security, the parameters of the synthesis network are replaced by a new parameter matrix based on a logistic map controlled by a secret key. The encryption key of the system is derived from the image content, which will be embedded in the deep feature vectors to save the cost of sending the key for different images. Another secret key, which will be shared among other images, is used for controlling the embedding process. And to learn the entropy model from the scrambled feature maps, an attention scheme is exploited in estimating parameters to achieve more effective compression. Experimental results and the security analysis using Kodak dataset show that the proposed scheme can achieve a sufficiently high-security level with high compression efficiency.