An image compression and encryption scheme for similarity retrieval

With the development of cloud computing, people usually outsource encrypted images for saving storage and protecting privacy. However, traditional image encryption methods not only hinder the availability of images such as similarity retrieval, but also degrade the compression performance. To address this issue, we propose a retrievable image compression and encryption method(RICE). RICE takes into account the contradiction of image compression, availability and security, then propose a cascaded information bottleneck model, which includes the compression information bottleneck and the security and availability information bottleneck. The former is converted into a rate distortion problem and its optimal solution is sought by a convolutional neural network(CNN)-based compression network which includes channel space attention module and discrete wavelet transform(DWT) module. To solve the later, we propose a feature partition method to find a retrieval subset that balances the contradiction between security and availability, and design a DNA-based deterministic encryption method for this subset to support ciphertext retrieval. The ciphertext of the retrieved subset is sent to the proposed similarity search fully connected network(SimFcNet) to improve the retrieval accuracy. The remaining subset is encrypted by Non-deterministic encryption to further improve security. In general, the method RICE we proposed supports similarity retrievable in compressed domain ciphertext, and can achieve excellent performance. Experimental results show that our method is 36.56% higher than JPEG2000 at compression ratio of 60:1 in MS-SSIM, the accuracy of ciphertext retrieval can reach 0.828, and the security of ciphertext is close to that of traditional encryption methods.