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decade, digital images are in use in a wide range of applications and for multiple purposes. They also play an important role in the storage and transfer of visual information, especially the secret ones. With this widespread usage of digital images, in addition to the increasing number of tools and software of digital images editing, it has become easy to manipulate and change the actual information of the image. In this detection technique used texture feature of image. For the texture extraction of image used wavelet transform function, these function is most promising texture analysis feature. For the selection of feature generation of pattern used clustering technique. Clustering technique is unsupervised learning technique process by iteration. The proposed methods are evaluated on a number of original and forged images. According to our experimental results the proposed methods are quite attractive. The forgery is done with just copy-move, copy-move with rotation, with scaling, and reflection. In this process, an image database that consists of original and forged images is also developed. The proposed method achieves 100% accuracy in just copy-move forgery (without any change in the size or characteristics of the object) forgery without post-processing and 98.43%, 86.58%, and 95.12% accuracies in copy-move forgery with rotation, scaling, and reflection, respectively.

Image Forgery Detection based on Local Descriptors and Block-Matching using Clustering Technique