Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Detection for Face Manipulation   It has become increasingly challenging to distinguish real faces from their
visually realistic fake counterparts, due to the great advances of deep
learning based face manipulation techniques in recent years. In this paper, we
introduce a deep learning method to detect face manipulation. It consists of
two stages: feature extraction and binary classification. To better distinguish
fake faces from real faces, we resort to the triplet loss function in the first
stage. We then design a simple linear classification network to bridge the
learned contrastive features with the real/fake faces. Experimental results on
public benchmark datasets demonstrate the effectiveness of this method, and
show that it generates better performance than state-of-the-art techniques in
most cases.

22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th