稀少的PGD:一个统一的框架稀少的对抗性扰乱生成
IEEE transactions on pattern analysis and machine intelligence
|November 7, 2025
概括
本研究介绍了Sparse-PGD,这是一种有效的方法,用于创建稀疏的对抗性扰动. 用这种技术训练的模型显示了对这些攻击的最先进的强度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 对抗性干扰对机器学习模型安全性构成重大威胁.
- 评估模型对稀疏扰动 (非结构化和结构化) 的稳定性至关重要.
研究的目的:
- 开发一个有效和高效的框架来产生稀疏的对抗性干扰.
- 综合评估对这些扰动的模型稳定性.
- 通过对抗训练来增强模型的稳定性.
主要方法:
- 提出了一种类似于白盒预测梯度下降 (PGD) 的攻击方法,名为Sparse-PGD.
- 结合Sparse-PGD与黑子攻击进行全面的稳定性评估.
- 使用Sparse-PGD进行对抗训练,以构建强大的模型.
主要成果:
- 在各种场景中,Sparse-PGD在产生稀疏的对抗性扰动方面表现出强的表现.
- 综合攻击方法提供了可靠的模型强度评估.
- 使用Sparse-PGD进行对抗训练显著提高了模型的弹性.
结论:
- 稀疏-PGD是一种有效和高效的工具,用于产生稀疏的对抗性扰动.
- 使用Sparse-PGD进行对抗训练,可以获得对稀疏攻击的最先进的强度.
- 拟议的框架提供了一种可靠的方法来评估和增强模型安全性.
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