Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Localization of JPEG double compression through multi-domain
  convolutional neural networks   When an attacker wants to falsify an image, in most of cases she/he will
perform a JPEG recompression. Different techniques have been developed based on
diverse theoretical assumptions but very effective solutions have not been
developed yet. Recently, machine learning based approaches have been started to
appear in the field of image forensics to solve diverse tasks such as
acquisition source identification and forgery detection. In this last case, the
aim ahead would be to get a trained neural network able, given a to-be-checked
image, to reliably localize the forged areas. With this in mind, our paper
proposes a step forward in this direction by analyzing how a single or double
JPEG compression can be revealed and localized using convolutional neural
networks (CNNs). Different kinds of input to the CNN have been taken into
consideration, and various experiments have been carried out trying also to
evidence potential issues to be further investigated.

27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th