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., Mazzola, G. and IEEE

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

https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651111879&doi=10.1109%2fICIP.2010.5652490&partnerID=40&md5=a26ec8bd8bc0a17cc57e9530766bf11f

Source: Manual