Generative Imperceptible Attack With Feature Learning Bias Reduction and Multi-Scale Variance Regularization-Reference-Cited by-同舟云学术

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Generative Imperceptible Attack With Feature Learning Bias Reduction and Multi-Scale Variance Regularization

Published:2024 Issue: Volume:19 Page:7924-7938
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Xie Weicheng¹^ORCID,Niu Zenghao¹^ORCID,Lin Qinliang¹,Song Siyang²^ORCID,Shen Linlin¹^ORCID

Affiliation:

1. Computer Vision Institute, School of Computer Science and Software Engineering, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen, China

2. School of Computer Science, University of Exeter, Exeter, U.K.

Funder

Natural Science Foundation of China

Science and Technology Project of Guangdong Province

Science and Technology Innovation Commission of Shenzhen

Guangdong Provincial Key Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/10206/10319981/10659041.pdf?arnumber=10659041

Reference66 articles.

1. Discriminator-free Generative Adversarial Attack

2. Measurement Reliability and Validity

3. Intriguing properties of neural networks;Szegedy

4. Explaining and harnessing adversarial examples;Goodfellow

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