Image Security Retrieval Based on Chaotic Algorithm and Deep Learning

Abstract

At present, most image retrieval methods are based on plain-text images which poses a threat to some professional fields, such as medicine, military, and finance. In order to achieve greater security for the network transmission security of the image, we establish a deep artificial neural network model to extract features by sample training. Then an image-encryption algorithm that matches and secures image retrieval is designed and integrated into an image-retrieval process based on deep learning. Experiments on multiple authoritative datasets show that the proposed algorithm can not only achieve secure retrieval of ciphertext images, but also improve retrieval efficiency obviously. Specifically, the experimental results on five data sets show that compared with the average performance of 16 comparison algorithms, each evaluation indicator has been significantly improved in our research, with Pre increased by 12.54% - 88.20%, Rec increased by 1.46% - 10.95%, F1 increased by 2.86% - 13.55% and mAP increased by 16.64% - 82.47%. Futhermore, the successful realization of the ciphertext retrieval provides some reference for the information security retrieval research.