Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detection of Face Recognition Adversarial Attacks   Deep Learning methods have become state-of-the-art for solving tasks such as
Face Recognition (FR). Unfortunately, despite their success, it has been
pointed out that these learning models are exposed to adversarial inputs -
images to which an imperceptible amount of noise for humans is added to
maliciously fool a neural network - thus limiting their adoption in real-world
applications. While it is true that an enormous effort has been spent in order
to train robust models against this type of threat, adversarial detection
techniques have recently started to draw attention within the scientific
community. A detection approach has the advantage that it does not require to
re-train any model, thus it can be added on top of any system. In this context,
we present our work on adversarial samples detection in forensics mainly
focused on detecting attacks against FR systems in which the learning model is
typically used only as a features extractor. Thus, in these cases, train a more
robust classifier might not be enough to defence a FR system. In this frame,
the contribution of our work is four-fold: i) we tested our recently proposed
adversarial detection approach against classifier attacks, i.e. adversarial
samples crafted to fool a FR neural network acting as a classifier; ii) using a
k-Nearest Neighbor (kNN) algorithm as a guidance, we generated deep features
attacks against a FR system based on a DL model acting as features extractor,
followed by a kNN which gives back the query identity based on features
similarity; iii) we used the deep features attacks to fool a FR system on the
1:1 Face Verification task and we showed their superior effectiveness with
respect to classifier attacks in fooling such type of system; iv) we used the
detectors trained on classifier attacks to detect deep features attacks, thus
showing that such approach is generalizable to different types of offensives.

29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex