现代信息科技2024，Vol.8Issue(11)：44-48,5.DOI:10.19850/j.cnki.2096-4706.2024.11.009
基于样本损失值变化统一性的后门样本隔离
Backdoor Sample Isolation Based on the Uniformity of Samples'Loss Value Changes
张家辉1
作者信息
- 1. 西安电子科技大学 网络与信息安全学院,陕西 西安 710126
- 折叠
摘要
Abstract
Backdoor attacks pose a potential threat to applying AI applications.Unlearning-based robust training methods achieve training models with no backdoor on untrusted datasets by isolating a subset of backdoor samples and unlearning it.However,incorrectly isolating and unlearning clean samples can lead to performance degradation of the model on clean data.In order to reduce false isolation of clean samples thus protecting model performance on clean data,a backdoor sample isolation scheme based on the uniformity of samples'loss value changes is proposed.During the training process,samples have large and uniform changes in loss value,and samples with low uniformity of loss value changes in the isolated and potential backdoor samples set are removed.Experimental results indicate that the application of the scheme benefits reducing the false isolation of clean samples,and protects the model's performance on clean data without compromising on defending against backdoor attacks.关键词
人工智能安全/后门防御/鲁棒训练/后门样本隔离/神经网络模型Key words
Artificial Intelligence security/backdoor defense/robust training/backdoor sample isolation/neural network model分类
信息技术与安全科学引用本文复制引用
张家辉..基于样本损失值变化统一性的后门样本隔离[J].现代信息科技,2024,8(11):44-48,5.
