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English(EN) Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks

新的DFCS方法将后门攻击效率提高了4.60% · 跟踪2个来源

研究人员开发了一种名为分布特征覆盖样本选择(DFCS)的新方法,以提高机器学习模型后门攻击的效率。这种无需训练、触发器无关的方法对预训练特征进行聚类,并从每个聚类中选择最近的样本,旨在避免冗余数据点。DFCS在各种数据集和攻击类型上展示了96.30%的高平均攻击成功率,平均比现有方法高出4.60个百分点,同时保持了干净的准确性。 AI

影响 增强了数据高效后门攻击的有效性,可能影响模型安全和数据完整性。

排序理由 该集群描述了一篇研究论文中提出的一种用于改进机器学习模型后门攻击的新方法。

在 arXiv cs.CV 阅读 →

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新的DFCS方法将后门攻击效率提高了4.60% · 跟踪2个来源

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该集群描述了一篇研究论文中提出的一种用于改进机器学习模型后门攻击的新方法。
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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    多样性至关重要:用于数据高效后门攻击的分布特征覆盖样本选择

    Backdoor attacks compromise training data so that a model retains clean accuracy but predicts an attacker-chosen target on triggered inputs. At very low poisoning rates, only a few samples convey the trigger--target association, making poison-sample selection critical. Existing m…

  2. arXiv cs.CV TIER_1 English(EN) · Yi Yang, Xiaoke Chen, Jinyang Huang, Feng-Qi Cui, Yu-Tong Guo, Jia-Cheng Zhao, Haiming Jin, Xiaokang Zhou, Meng Li ·

    多样性很重要:数据高效后门攻击的分布特征覆盖样本选择

    arXiv:2608.09047v1 Announce Type: cross Abstract: Backdoor attacks compromise training data so that a model retains clean accuracy but predicts an attacker-chosen target on triggered inputs. At very low poisoning rates, only a few samples convey the trigger--target association, m…