Transferable Adversarial Example Generation Method For Face Verification

SUN Junmei; PAN Zhenxiong; LI Xiumei; YUAN Long; ZHANG Xin

doi:10.11999/JEIT220358

Volume 45 Issue 5

May 2023

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SUN Junmei, PAN Zhenxiong, LI Xiumei, YUAN Long, ZHANG Xin. Transferable Adversarial Example Generation Method For Face Verification[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1842-1851. doi: 10.11999/JEIT220358

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SUN Junmei, PAN Zhenxiong, LI Xiumei, YUAN Long, ZHANG Xin. Transferable Adversarial Example Generation Method For Face Verification[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1842-1851. doi: 10.11999/JEIT220358

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Transferable Adversarial Example Generation Method For Face Verification

doi: 10.11999/JEIT220358

School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China

Funds: The National Natural Science Foundation of China (61801159, 61571174), The Science and Technology Plan Project of Hangzhou (20201203B124)

Received Date: 2022-03-31
Accepted Date: 2022-09-06
Rev Recd Date: 2022-08-26

Available Online: 2022-09-09

Publish Date: 2023-05-10

Abstract

Abstract

In the face verification task of the face recognition model, traditional adversarial attack methods can not quickly generate real and natural adversarial examples, and the adversarial examples generated for one model under the white-box setting perform worse when transferred to other models. A GAN-based method TAdvFace is proposed for transferable adversarial example generation. TAdvFace uses an attention generator to improve the extraction of facial features. A Gaussian filtering operation is used to improve the smoothness of the adversarial samples. An automatic adjustment strategy is used to adjust the loss weight of identity discrimination, which can quickly generate high-quality migratable adversarial samples based on different face images. Experimental results show that through the white box training of a single model, the adversarial examples generated by the TAdvFace can achieve great attack results and transferability in a variety of face recognition models and commercial API models.
- Face verification,
- Adversarial example,
- Generate adversarial networks,
- Transferability

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References(21)

References

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