Attacks on state-of-the-art face recognition using attentional adversarial attack generative network
With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face recognition networks are subject to attacks. Generating adversarial examples is an effective attack method, which misleads the face recognition sys...
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| Published in | Multimedia tools and applications Vol. 80; no. 1; pp. 855 - 875 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.01.2021
Springer Nature B.V |
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| Online Access | Get full text |
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| Abstract | With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face recognition networks are subject to attacks. Generating adversarial examples is an effective attack method, which misleads the face recognition system through obfuscation attack (rejecting a genuine subject) or impersonation attack (matching to an impostor). In this paper, we introduce a novel GAN, Attentional Adversarial Attack Generative Network (
A
3
G
N
), to generate adversarial examples that mislead the network to identify someone as the target person not misclassify inconspicuously. For capturing the geometric and context information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces a face recognition network as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person. |
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| AbstractList | With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face recognition networks are subject to attacks. Generating adversarial examples is an effective attack method, which misleads the face recognition system through obfuscation attack (rejecting a genuine subject) or impersonation attack (matching to an impostor). In this paper, we introduce a novel GAN, Attentional Adversarial Attack Generative Network (
A
3
G
N
), to generate adversarial examples that mislead the network to identify someone as the target person not misclassify inconspicuously. For capturing the geometric and context information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces a face recognition network as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person. With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face recognition networks are subject to attacks. Generating adversarial examples is an effective attack method, which misleads the face recognition system through obfuscation attack (rejecting a genuine subject) or impersonation attack (matching to an impostor). In this paper, we introduce a novel GAN, Attentional Adversarial Attack Generative Network (A3GN), to generate adversarial examples that mislead the network to identify someone as the target person not misclassify inconspicuously. For capturing the geometric and context information of the target person, this work adds a conditional variational autoencoder and attention modules to learn the instance-level correspondences between faces. Unlike traditional two-player GAN, this work introduces a face recognition network as the third player to participate in the competition between generator and discriminator which allows the attacker to impersonate the target person better. The generated faces which are hard to arouse the notice of onlookers can evade recognition by state-of-the-art networks and most of them are recognized as the target person. |
| Author | Yang, Lu Wu, Yingqi Song, Qing |
| Author_xml | – sequence: 1 givenname: Lu surname: Yang fullname: Yang, Lu organization: Pattern Recognition and Intelligence Vision Lab, Beijing University of Posts and Telecommunications – sequence: 2 givenname: Qing surname: Song fullname: Song, Qing email: priv@bupt.edu.cn organization: Pattern Recognition and Intelligence Vision Lab, Beijing University of Posts and Telecommunications – sequence: 3 givenname: Yingqi surname: Wu fullname: Wu, Yingqi organization: Pattern Recognition and Intelligence Vision Lab, Beijing University of Posts and Telecommunications |
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| Keywords | Face recognition Adversarial attack Generative adversarial networks |
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| Snippet | With the broad use of face recognition, its weakness gradually emerges that it is able to be attacked. Therefore, it is very important to study how face... |
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| SubjectTerms | Computer Communication Networks Computer Science Data Structures and Information Theory Face recognition Multimedia Multimedia Information Systems Neural networks Special Purpose and Application-Based Systems Target recognition |
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| Title | Attacks on state-of-the-art face recognition using attentional adversarial attack generative network |
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