Improving Transferability of Adversarial Samples via Critical Region-Oriented Feature-Level Attack-Reference-Cited by-同舟云学术

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Improving Transferability of Adversarial Samples via Critical Region-Oriented Feature-Level Attack

Published:2024 Issue: Volume:19 Page:6650-6664
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Li Zhiwei¹^ORCID,Ren Min²^ORCID,Li Qi¹^ORCID,Jiang Fangling³^ORCID,Sun Zhenan¹^ORCID

Affiliation:

1. New Laboratory of Pattern Recognition and the State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

2. School of Artificial Intelligence, Beijing Normal University, Beijing, China

3. School of Computer Science, University of South China, Hengyang, Hunan, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Beijing Municipal Natural Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10538111.pdf?arnumber=10538111

Reference72 articles.

1. ImageNet classification with deep convolutional neural networks

2. Densely Connected Convolutional Networks

3. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

4. Understanding Humans in Crowded Scenes

5. Deep supervised discrete hashing;Li

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