Copy-move forgery detection using SURF feature extraction and SVM supervised learning technique

Abstract

The goal of computer vision is to identify objects of interest from images. Copy-move forgery is the process of copying one or more parts of an image and moved into another part of an equivalent image. The detection of copy-move images has many limitations in recognition of objects. In this paper, the proposed work uses Speeded Up Robust Feature (SURF) feature extraction, and the specific object is recognized with the help of the support vector machine. When copy-move forgery was performed, some modifications were done to the image. For instance, turning, scaling, darkening, compression, and noise addition are applied to make effective impersonation forgeries. Here, feature matching process uses the image rotate function, which consists of bicubic and crop operations, and calculates the difference using the blend, scale and joint operation. The results show that forged images are extracted from a given set of test images. The test results exhibit that the proposed technique can get noteworthy and impressive results.