Certainty Attacks Using Explainability Preprocessing

Carina Newen

doi:10.1007/978-3-032-02215-8_15

2026-01-01

PRE

Carina Newen *

Sofia Vergara Puccini

Emmanuel Müller

DOI：10.1007/978-3-032-02215-8_15

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随着机器学习的重要性逐年提升，学习者已根据其在未见数据上的表现进行了优化，但并未考虑待分类数据可能被攻击者主动操纵以引发误判。本研究提出了一种针对模型置信度的攻击方法。我们认为这是一种重要的攻击角度，因为许多检测防御措施依赖于置信度指标。本文的新颖之处在于，我们在四个关键方面优化了攻击：成功率、误分类的置信度、攻击向其他模型的迁移能力以及生成对抗样本的图像质量。我们将其作为一种改进攻击的方法，以提升攻击的置信度及其他关键指标（如攻击效果），而不仅限于准确率。代码可从https://github.com/KDDOpenSource/Certainty-Attacks.git获取。

Keyword:

Certainty Attacks

Adversarial Examples

Explainability Preprocessing

Model Robustness

Transferability

期刊

BIG DATA ANALYTICS AND KNOWLEDGE DISCOVERY, DAWAK 2025

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dortmund university of technology

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9.4K

论文数: 9.1K

被引数: 15

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