Coarse-to-Fine Secure Image Deduplication with Merkle-Hash and Image Features for Cloud Storage

Studies show that the total digital resource has already exceeded the global storage capacity due to the information explosion due to the speed of the development of digital data. It has become crucial to store this data securely and effectively. The deduplication concept helps to identify redundant data which can be removed leaving only one copy. To handle this deduplication authentically, we propose a three-phase coarse-to-fine model to identify duplicate images in a faster and efficient way for cloud image data storage. The phases are Global features based, Local features based, and Merkle hash tree-based image deduplication. It is observed that the proposed model is fast and efficient.