A secure lossless recovery for medical images based on image encoding and data self-embedding

Abstract

To protect the security of data outsourced to the cloud, the tampers detection and recovery for outsourced image have aroused the concern of people. A secure tampering detection and lossless recovery for medical images (MI) using permutation ordered binary (POB) number system is proposed. In the proposed scheme, the region of interest (ROI) of MI is first extracted, and then, ROI is divided into some no-overlapping blocks, and image encoding is conducted on these blocks based on the better compression performance of JPEG-LS for medical image. After that, the generated compression data by all the blocks are divided into high 4-bit and low 4-bit planes, and shuffling and combination are used to generate two plane images. Owing to the substantial redundancies space in the compressed data, the data of each plane are spread to the size of the original image. Lastly, authentication data of two bits is obtained for every pixel and inserted into the pixel itself within the each plane, and the corresponding 10-bit data is transformed into the POB value of 8-bit. Furthermore, encryption is implemented on the above image to produce two shares which can be outsourced to the cloud server. The users can detect tampered part and recover original image when they down load the shares from the cloud. Extensive experiments on some ordinary medical image and COVID-19 image datasets show that the proposed approach can locate the tampered parts within the MI, and the original MI can be recovered without any loss even if one of the shares are totally destroyed, or two shares are tampered at the ration not more than 50%. Some comparisons and analysis are given to show the better performance of the scheme.