Optimizied techniques for image hashing

For the identification and verification of image contents, extremely efficient automated techniques are needed that require digital images to be processed and propagated. Digital data is prone to attacks leaving absolutely no clues about the attacker. Image hashing is the ability to categorize an image irrespective of the change in features like resolution and format as well as corruption. It is used to identify and verify images with similar structural content. We make use of the hash function which purposes to extract a fixed length bit code from a text or image. Hash functions have found varied applications in cryptography, video surveillance in addition to rummage the database. In this review paper, a detailed comparison of three techniques of image hashing is performed which include the Discrete Wavelet Transform, Singular Value Decomposition and a third technique of optimized Feature Extraction. The results of our experimentation reveal optimized values of Feature extraction as the best technique for image hashing.