Exact Image Deduplication using Hashing Techniques on cloud repositories

As digital images grow exponentially, duplicate detection and removal are necessary for effective storage management and data organization. The conventional approaches of pixel-by-pixel comparison or metadata analysis are computationally intensive and fail to identify transformed duplicates such as resized or color-manipulated images. To overcome these shortcomings, this study presents a strong and scalable solution employing perceptual hashing methods such as Difference Hash, Average Hash, Perceptual Hash, and Cryptographic hashing. These algorithms create short hash values that retain crucial visual characteristics, making it possible to detect similar images even when they have been transformed. The suggested system focuses on delivering a robust and efficient solution for the detection of duplicate images, alleviating the inefficiencies of the current methods and proposing a multiparameter decisionmaking strategy for duplicate detection through hashing methods.