Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-Intrusive Detection of Adversarial Deep Learning Attacks via
  Observer Networks   Recent studies have shown that deep learning models are vulnerable to
specifically crafted adversarial inputs that are quasi-imperceptible to humans.
In this letter, we propose a novel method to detect adversarial inputs, by
augmenting the main classification network with multiple binary detectors
(observer networks) which take inputs from the hidden layers of the original
network (convolutional kernel outputs) and classify the input as clean or
adversarial. During inference, the detectors are treated as a part of an
ensemble network and the input is deemed adversarial if at least half of the
detectors classify it as so. The proposed method addresses the trade-off
between accuracy of classification on clean and adversarial samples, as the
original classification network is not modified during the detection process.
The use of multiple observer networks makes attacking the detection mechanism
non-trivial even when the attacker is aware of the victim classifier. We
achieve a 99.5% detection accuracy on the MNIST dataset and 97.5% on the
CIFAR-10 dataset using the Fast Gradient Sign Attack in a semi-white box setup.
The number of false positive detections is a mere 0.12% in the worst case
scenario.

29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga