Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Shape Defense Against Adversarial Attacks   Humans rely heavily on shape information to recognize objects. Conversely,
convolutional neural networks (CNNs) are biased more towards texture. This is
perhaps the main reason why CNNs are vulnerable to adversarial examples. Here,
we explore how shape bias can be incorporated into CNNs to improve their
robustness. Two algorithms are proposed, based on the observation that edges
are invariant to moderate imperceptible perturbations. In the first one, a
classifier is adversarially trained on images with the edge map as an
additional channel. At inference time, the edge map is recomputed and
concatenated to the image. In the second algorithm, a conditional GAN is
trained to translate the edge maps, from clean and/or perturbed images, into
clean images. Inference is done over the generated image corresponding to the
input's edge map. Extensive experiments over 10 datasets demonstrate the
effectiveness of the proposed algorithms against FGSM and $\ell_\infty$ PGD-40
attacks. Further, we show that a) edge information can also benefit other
adversarial training methods, and b) CNNs trained on edge-augmented inputs are
more robust against natural image corruptions such as motion blur, impulse
noise and JPEG compression, than CNNs trained solely on RGB images. From a
broader perspective, our study suggests that CNNs do not adequately account for
image structures that are crucial for robustness. Code is available
at:~\url{https://github.com/aliborji/Shapedefense.git}.

18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th