Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TESDA: Transform Enabled Statistical Detection of Attacks in Deep Neural
  Networks   Deep neural networks (DNNs) are now the de facto choice for computer vision
tasks such as image classification. However, their complexity and "black box"
nature often renders the systems they're deployed in vulnerable to a range of
security threats. Successfully identifying such threats, especially in
safety-critical real-world applications is thus of utmost importance, but still
very much an open problem. We present TESDA, a low-overhead, flexible, and
statistically grounded method for {online detection} of attacks by exploiting
the discrepancies they cause in the distributions of intermediate layer
features of DNNs. Unlike most prior work, we require neither dedicated hardware
to run in real-time, nor the presence of a Trojan trigger to detect
discrepancies in behavior. We empirically establish our method's usefulness and
practicality across multiple architectures, datasets and diverse attacks,
consistently achieving detection coverages of above 95% with operation count
overheads as low as 1-2%.

8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga