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Deeply Supervised Discriminative Learning for Adversarial Defense

Deep neural networks can easily be fooled by an adversary with minuscule perturbations added to an input image. The existing defense techniques suffer greatly under white-box attack settings, where an adversary has full knowledge of the network and can iterate several times to find strong perturbations. We observe that the main reason for the existence of such vulnerabilities is the close proximity of different class samples in the learned feature space of deep models. This allows the model decisions to be completely changed by adding an imperceptible perturbation to the inputs. To counter this, we propose to class-wise disentangle the intermediate feature representations of deep networks, specifically forcing the features for each class to lie inside a convex polytope that is maximally separated from the polytopes of other classes. In this manner, the network is forced to learn distinct and distant decision regions for each class. We observe that this simple constraint on the features greatly enhances the robustness of learned models, even against the strongest white-box attacks, without degrading the classification performance on clean images. We report extensive evaluations in both black-box and white-box attack scenarios and show significant gains in comparison to state-of-the-art defenses.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.43 No.9 September 2021

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 3154 - 3166

Bahasa

English

ISBN/ISSN

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Klasifikasi

-

Tipe Isi

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Tipe Media

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Tipe Pembawa

-

Edisi

-

Subjek

DISTANCE METRIC LEARNING
ADVERSARIAL DEFENSE
ADVERSARIAL ROBUSTNESS
WHITE-BOX ATTACK
DEEP SUPERVISION

Info Detail Spesifik

DOI: 10.1109/TPAMI.2020.2978474

Pernyataan Tanggungjawab

Aamir Mustafa, Salman H. Khan, Munawar Hayat, Roland Goecke, Jianbing Shen, Ling Shao

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