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JPEG compression model in copy-move forgery detection

Authors: Adam Novozámský; Michal Sorel;

JPEG compression model in copy-move forgery detection

Abstract

The integrity of visual data is important for the credibility of news media and especially when used as an evidence in court or during criminal investigation. The common way to manipulate image content is copying an object and pasting in another location of the same image. In this paper, we describe a new idea for the detection of this type of forgery in JPEG images, where the compression significantly degrades detection by popular algorithms. We derive a JPEG-based constraint that any pair of patches must satisfy to be considered a valid candidate for tampered area. We propose also efficient algorithm to verify the constraint that can be integrated into most existing methods. Experiments show significant improvement of detection, especially for difficult cases, such as small objects, objects covered by textureless areas and repeated patterns.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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