Downstream-agnostic Adversarial Examples-Reference-Cited by-同舟云学术

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Downstream-agnostic Adversarial Examples

Published:2023-10-01 Issue: Volume:23 Page:4322-4332
ISSN:
Container-title:2023 IEEE/CVF International Conference on Computer Vision (ICCV)
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Author:

Zhou Ziqi¹,Hu Shengshan¹,Zhao Ruizhi¹,Wang Qian²,Zhang Leo Yu³,Hou Junhui⁴,Jin Hai⁵

Affiliation:

1. Huazhong University of Science and Technology,School of Cyber Science and Engineering

2. Wuhan University,School of Cyber Science and Engineering

3. Griffith University,School of Information and Communication Technology

4. City University of Hong Kong,Department of Computer Science

5. Huazhong University of Science and Technology,School of Computer Science and Technology

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10376473/10376477/10378625.pdf?arnumber=10378625

Reference67 articles.

1. Pre-trained adversarial perturbations;Ban

2. Towards Evaluating the Robustness of Neural Networks

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1. A proxy-data-based hierarchical adversarial patch generation method;Computer Vision and Image Understanding;2024-09

2. Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

3. Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

4. How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses;IEEE Access;2024

5. Discrepant Semantic Diffusion Boosts Transfer Learning Robustness;Electronics;2023-12-16

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