Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Human Social Cognition for the Detection of Fake and
  Fraudulent Faces via Memory Networks   Advances in computer vision have brought us to the point where we have the
ability to synthesise realistic fake content. Such approaches are seen as a
source of disinformation and mistrust, and pose serious concerns to governments
around the world. Convolutional Neural Networks (CNNs) demonstrate encouraging
results when detecting fake images that arise from the specific type of
manipulation they are trained on. However, this success has not transitioned to
unseen manipulation types, resulting in a significant gap in the
line-of-defense. We propose a Hierarchical Memory Network (HMN) architecture,
which is able to successfully detect faked faces by utilising knowledge stored
in neural memories as well as visual cues to reason about the perceived face
and anticipate its future semantic embeddings. This renders a generalisable
face tampering detection framework. Experimental results demonstrate the
proposed approach achieves superior performance for fake and fraudulent face
detection compared to the state-of-the-art.

8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph