Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incremental learning for the detection and classification of
  GAN-generated images   Current developments in computer vision and deep learning allow to
automatically generate hyper-realistic images, hardly distinguishable from real
ones. In particular, human face generation achieved a stunning level of
realism, opening new opportunities for the creative industry but, at the same
time, new scary scenarios where such content can be maliciously misused.
Therefore, it is essential to develop innovative methodologies to automatically
tell apart real from computer generated multimedia, possibly able to follow the
evolution and continuous improvement of data in terms of quality and realism.
In the last few years, several deep learning-based solutions have been proposed
for this problem, mostly based on Convolutional Neural Networks (CNNs).
Although results are good in controlled conditions, it is not clear how such
proposals can adapt to real-world scenarios, where learning needs to
continuously evolve as new types of generated data appear. In this work, we
tackle this problem by proposing an approach based on incremental learning for
the detection and classification of GAN-generated images. Experiments on a
dataset comprising images generated by several GAN-based architectures show
that the proposed method is able to correctly perform discrimination when new
GANs are presented to the network

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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
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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: ao-sci