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Robust Near Duplicate Image Matching for Digital Image Forensics

Robust Near Duplicate Image Matching for Digital Image Forensics

H.R. Chennamma, Lalitha Rangarajan, M.S. Rao
Copyright: © 2009 |Volume: 1 |Issue: 3 |Pages: 18
ISSN: 1941-6210|EISSN: 1941-6229|ISSN: 1941-6210|EISBN13: 9781616920821|EISSN: 1941-6229|DOI: 10.4018/jdcf.2009070104
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MLA

Chennamma, H.R., et al. "Robust Near Duplicate Image Matching for Digital Image Forensics." IJDCF vol.1, no.3 2009: pp.62-79. http://doi.org/10.4018/jdcf.2009070104

APA

Chennamma, H., Rangarajan, L., & Rao, M. (2009). Robust Near Duplicate Image Matching for Digital Image Forensics. International Journal of Digital Crime and Forensics (IJDCF), 1(3), 62-79. http://doi.org/10.4018/jdcf.2009070104

Chicago

Chennamma, H.R., Lalitha Rangarajan, and M.S. Rao. "Robust Near Duplicate Image Matching for Digital Image Forensics," International Journal of Digital Crime and Forensics (IJDCF) 1, no.3: 62-79. http://doi.org/10.4018/jdcf.2009070104

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Abstract

Local invariant key point extraction has recently emerged as an attractive approach for detecting near duplicate images. Near duplicate images can be: (i) perceptually identical images (e.g. allowing for change in color balance, change in brightness, compression artifacts, contrast adjustment, rotation, cropping, filtering, scaling etc.), (ii) images of the same 3D scene (from different viewpoints). The requirements for identifying near duplicate images vary according to the application. In this paper we focus on image matching strategy that will assist in the detection of forged (copy-paste forgery) images. So far, no specific image matching strategy exists for this application. The state of the art methodologies tend to generate many false positives. In this paper we have introduced a novel matching strategy for pattern matching of key point distributions. Typical experiments conducted with real world images demonstrate success in near duplicate image retrieval for the application of digital image forensic. Proposed method outperforms some of the existing methods and is computationally efficient.

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