Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: WaveTransform: Crafting Adversarial Examples via Input Decomposition   Frequency spectrum has played a significant role in learning unique and
discriminating features for object recognition. Both low and high frequency
information present in images have been extracted and learnt by a host of
representation learning techniques, including deep learning. Inspired by this
observation, we introduce a novel class of adversarial attacks, namely
`WaveTransform', that creates adversarial noise corresponding to low-frequency
and high-frequency subbands, separately (or in combination). The frequency
subbands are analyzed using wavelet decomposition; the subbands are corrupted
and then used to construct an adversarial example. Experiments are performed
using multiple databases and CNN models to establish the effectiveness of the
proposed WaveTransform attack and analyze the importance of a particular
frequency component. The robustness of the proposed attack is also evaluated
through its transferability and resiliency against a recent adversarial defense
algorithm. Experiments show that the proposed attack is effective against the
defense algorithm and is also transferable across CNNs.

22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex