Post-train black-box defense through energy-based Bayesian adversarial training

Authors

Yingxue Diao (ORCID: https://orcid.org/0000-0002-6007-4795)
Dan Guo
Zhuyin Qian (ORCID: https://orcid.org/0000-0002-9709-4829)
Meng Wang (ORCID: https://orcid.org/0000-0002-7481-1856)
Zhiyong Liang (ORCID: https://orcid.org/0000-0003-4787-2925)
Kui Jiang (ORCID: https://orcid.org/0000-0002-5487-928X)
Zhuofan Shi (ORCID: https://orcid.org/0000-0002-6769-687X)

Publication Details

Journal: Scientia Sinica Informationis
Published: 2025-04-14
DOI: https://doi.org/10.1360/ssi-2024-0326
Citations: 1
Primary Topic: Adversarial Robustness in Machine Learning
Type: article
Field-Weighted Citation Impact: 2.48

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article

Post-train black-box defense through energy-based Bayesian adversarial training

Yingxue Diao, Dan Guo, Zhuyin Qian, Meng Wang et al.

Apr 14, 2025

1 citations

Scientia Sinica Informationis

Adversarial Robustness in Machine Learning

2.48

article

Post-train black-box defense through energy-based Bayesian adversarial training

Yingxue Diao, Dan Guo, Zhuyin Qian, Meng Wang, Zhiyong Liang, Kui Jiang, Zhuofan Shi

April 14, 2025article en

1 citations

Abstract

No abstract available for this paper.

Scientia Sinica InformationisVol. 55(8)

Openalex Percentile: Top 11%

Adversarial Robustness in Machine Learning

2.48

DOI Open Access PDF OpenAlex

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Post-train black-box defense through energy-based Bayesian adversarial training — Yingxue Diao, Dan Guo, et al. · Scientia Sinica Informationis (2025) | TGRS Research Map | TGRS