Detecting multiple copies in tampered images
Authors: Ardizzone, E., Bruno, A. and Mazzola, G.
Journal: Proceedings - International Conference on Image Processing, ICIP
Pages: 2117-2120
ISBN: 9781424479948
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5652490
Abstract:Copy-move forgeries are parts of the image that are duplicated elsewhere into the same image, often after being modified by geometrical transformations. In this paper we present a method to detect these image alterations, using a SIFT-based approach. First we describe a state of the art SIFT-point matching method, which inspired our algorithm, then we compare it with our SIFT-based approach, which consists of three parts: keypoint clustering, cluster matching, and texture analysis. The goal is to find copies of the same object, i.e. clusters of points, rather than points that match. Cluster matching proves to give better results than single point matching, since it returns a complete and coherent comparison between copied objects. At last, textures of matching areas are analyzed and compared to validate results and to eliminate false positives. © 2010 IEEE.
Source: Scopus
DETECTING MULTIPLE COPIES IN TAMPERED IMAGES
Authors: Ardizzone, E., Bruno, A. and Mazzola, G.
Journal: 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING
Pages: 2117-2120
ISSN: 1522-4880
DOI: 10.1109/ICIP.2010.5652490
Source: Web of Science (Lite)
Detecting multiple copies in tampered images
Authors: Ardizzone, E., Bruno, A. and Mazzola, G.
Pages: 2117-2120
DOI: 10.1109/ICIP.2010.5652490
Source: Manual