PSO and SURF based digital image forgery detection

Digital images have become very important in our daily lives and some other important areas such as medicine, journalism and it can be also used as forensic evidence. However, the simplicity of using digital images with freely available software tools makes the authenticity of images questionable. The most common image forgery type is copy move forgery because it can be done easily but the detection of this type of forgery is hard. Various approaches are proposed in literature to detection of copy move forgery, but lots of them is not satisfy result especially smooth regions are used to hide objects. And lots of works use experience parameters values so sometimes they cannot detect forgery operations. To solve these problems we proposed an optimized keypoint based copy move forgery detection methods based on Speeded-Up Robust Features (SURF) algorithm and Particle Swarm Optimization (PSO). Experimental results show that the proposed method has good performance even under post processing and preprocessing attacks (such as blurring, noise addition, rotation, JPEG compression).