Proficient Hash Sensitivity Growth for Optimized Image Forgery Detection

Abstract

Abstract:The motive behind the work is to provide an effective solution to the most sensitive issue called image forgery that is occurring due to increase in an availability of enormous image modification software. The image forgery causes drastic bad effects in the society such as copyright misuse, evident change in the court of law, quality control, medical image forgery, etc. There are numerous steps taken in order to detect forgery in images, but how far they are successful is the question here. In this paper, an advanced and efficient solution is provided for the forgery detection which can overcome the drawbacks of the existing works by accurately detecting the salient regions by considering both the local and global features of an image (when considering the whole image it is global and when considering only the specific part of an image it is local) and based on this a technique is proposed called hash sensitivity growth method (HSGM), which can accurately detect the salient regions of an image and extract feature contents from that region, hence provide efficient sensitivity growth to a hash, as the sensitivity of the hash is increased it can accurately detect even smaller area tampering and it is robust to normal image processing.