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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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通过特征增强来提高对抗性示例的可转移性.

Donghua Wang, Wen Yao, Tingsong Jiang

    IEEE transactions on neural networks and learning systems
    |May 8, 2025
    PubMed
    概括

    这项研究引入了特征增强攻击 (FAUG),以增强对手的可转移性. FAUG 改进了对抗性的例子.

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习安全 机器学习安全

    背景情况:

    • 对抗性可转移性使对抗性示例能够攻击未知的模型.
    • 模型架构的差异可能会削弱对抗性的可转移性.
    • 现有的方法,如输入转换和整体攻击有局限性 (忽视模型差异,资源密集).

    研究的目的:

    • 提出一种简单有效的特征增强攻击 (FAUG) 方法,以提高对抗性可转移性.
    • 在对抗示例生成期间,动态添加中介特征的随机噪声,以防止过度拟合.

    主要方法:

    • 通过不同层和不同噪声强度的研究模型噪声耐受性.
    • 设计了一种基于小批次特征的动态随机噪声生成方法.
    • 在功能增强模型上应用了基于梯度的攻击算法.

    主要成果:

    • 在没有额外的计算成本的情况下,FAUG提高了对抗性可转移性.
    • 取得了显著的改进:+30.67%的基于输入转换的攻击和+5.57%的组合方法.
    • 通过CNN和变压器模型在ImageNet上的实验验验证了该方法的有效性.

    结论:

    • 特性增强攻击 (FAUG) 是一种有效的技术,可以提高对手的可转移性.

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  • 拟议的动态噪声注入方法成功地减轻了对目标模型的过度装配.
  • FAUG提供了一个实用的解决方案,用于创建针对各种模型的更强大的对抗示例.