An Efficient Forensic Method Based on High-speed Corner Detection Technique and SIFT Descriptor

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10040)

Abstract

Image manipulation has become commonplace with growing easy access to sophisticated photo editing softwares. One of the most common types of image forgeries is the copy–move forgery, wherein a region from an image is replaced with another region from the same image. Many existing forensic methods suffer from their inability to detect the cloned area, which is subjected to various transformations such as scaling, rotation, flipping and blurring. In this paper, we propose a novel forensic method based on high-speed corner detection (HSCD) technique and improved scale invariant features transform (SIFT) descriptor. Machine learning technique is used to detect feature points which greatly decreasing the processing time compare to other feather detectors. Experimental results show the efficacy of this technique in detecting copy-move forgeries and estimating the geometric transformation parameters. Compared with the state of the art, our approach obtains a higher true positive rate and a lower false positive rate.

Keywords

Image forensics High-speed corner detection Copy-move forgery detection SIFT Descriptor 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of DesignJiangnan UniversityWuxiChina

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