Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boosting Image Forgery Detection using Resampling Features and Copy-move
  analysis   Realistic image forgeries involve a combination of splicing, resampling,
cloning, region removal and other methods. While resampling detection
algorithms are effective in detecting splicing and resampling, copy-move
detection algorithms excel in detecting cloning and region removal. In this
paper, we combine these complementary approaches in a way that boosts the
overall accuracy of image manipulation detection. We use the copy-move
detection method as a pre-filtering step and pass those images that are
classified as untampered to a deep learning based resampling detection
framework. Experimental results on various datasets including the 2017 NIST
Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and
tampered images shows that there is a consistent increase of 8%-10% in
detection rates, when copy-move algorithm is combined with different resampling
detection algorithms.

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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: q-bio