Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning JPEG Compression Artifacts for Image Manipulation Detection and
  Localization   Detecting and localizing image manipulation are necessary to counter
malicious use of image editing techniques. Accordingly, it is essential to
distinguish between authentic and tampered regions by analyzing intrinsic
statistics in an image. We focus on JPEG compression artifacts left during
image acquisition and editing. We propose a convolutional neural network (CNN)
that uses discrete cosine transform (DCT) coefficients, where compression
artifacts remain, to localize image manipulation. Standard CNNs cannot learn
the distribution of DCT coefficients because the convolution throws away the
spatial coordinates, which are essential for DCT coefficients. We illustrate
how to design and train a neural network that can learn the distribution of DCT
coefficients. Furthermore, we introduce Compression Artifact Tracing Network
(CAT-Net) that jointly uses image acquisition artifacts and compression
artifacts. It significantly outperforms traditional and deep neural
network-based methods in detecting and localizing tampered regions.

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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg