Copy Move Forgery Detection Using Hybrid Algorithm

Abstract

Image forgery detection in terms of copy-move detection is an emerging area for researchers in recent times. This paper proposes the combined methodology of Saliency detection and Local binary pattern-based forgery detection for real time images. Saliency detection is used to identify the forged portion on a preliminary step. It locks the region of the tampered portion with surrounding (global) area. Then, the exact pixels/region is identified or captured by Local Binary Pattern features for the tampered portions. This combined approach maintains the advantages of both saliency map and Local Binary Pattern particularly in scaling/rotation with fast detection rate than the existing methods. In addition to the existing outcome of forgery detection, the proposed method accurately identifies the forged region with depth map information. The severity of the tampered portion is examined with reference to the bits/pixel. The novelty in the proposed method lays the way of detecting the tampered portion in the real-time digital image. The state of art comparison is also proved that the proposed method is far better than existing methods.