Automatic Tampering Detection in Spliced Images with Different Compression Levels View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Diego García-Ordás , Laura Fernández-Robles , Enrique Alegre , María Teresa García-Ordás , Oscar García-Olalla

ABSTRACT

In this paper, we introduce a blind tampering detection method based on JPEG ghosts [3] capable of detecting tampering when it is created by splicing regions with different compression levels in an image. Given an image, a set of re-compressions of that image is generated and used to extract a feature vector to train a Support Vector Machine classifier. We used two different datasets in our experiments. The first one, extracted from Columbia Uncompressed Image Splicing Detection, was used to compare results with other works. Our method outperformed the previous ones when dealing with small tampered regions and similar qualities, offering hit rates above 97% (100% in the case of non-tampered images). With the second dataset, CASIA1 Tampered Image Detection Evaluation Dataset, our method offered a hit rate of 98.71% when discerning between the original and the spliced image, with just a 0.44% of non-tampered images wrongly classified. More... »

PAGES

416-423

Book

TITLE

Pattern Recognition and Image Analysis

ISBN

978-3-642-38627-5
978-3-642-38628-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-38628-2_49

DOI

http://dx.doi.org/10.1007/978-3-642-38628-2_49

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1033598014


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