AdvFoolGen: Creating Persistent Troubles for Deep Classifiers
- URL: http://arxiv.org/abs/2007.10485v1
- Date: Mon, 20 Jul 2020 21:27:41 GMT
- Title: AdvFoolGen: Creating Persistent Troubles for Deep Classifiers
- Authors: Yuzhen Ding, Nupur Thakur, Baoxin Li
- Abstract summary: We present a new black-box attack termed AdvFoolGen, which can generate attacking images from the same feature space as that of the natural images.
We demonstrate the effectiveness and robustness of our attack in the face of state-of-the-art defense techniques.
- Score: 17.709146615433458
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Researches have shown that deep neural networks are vulnerable to malicious
attacks, where adversarial images are created to trick a network into
misclassification even if the images may give rise to totally different labels
by human eyes. To make deep networks more robust to such attacks, many defense
mechanisms have been proposed in the literature, some of which are quite
effective for guarding against typical attacks. In this paper, we present a new
black-box attack termed AdvFoolGen, which can generate attacking images from
the same feature space as that of the natural images, so as to keep baffling
the network even though state-of-the-art defense mechanisms have been applied.
We systematically evaluate our model by comparing with well-established attack
algorithms. Through experiments, we demonstrate the effectiveness and
robustness of our attack in the face of state-of-the-art defense techniques and
unveil the potential reasons for its effectiveness through principled analysis.
As such, AdvFoolGen contributes to understanding the vulnerability of deep
networks from a new perspective and may, in turn, help in developing and
evaluating new defense mechanisms.
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