L-GEM BASED ROBUST LEA ING AGAINST POISONING ATTACK

被引:0
|
作者
Zhang, Fei [1 ,2 ]
Chan, Patrick P. K. [1 ]
Tang, Tian-Qi [1 ]
机构
[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou, Guangdong, Peoples R China
[2] Henan Normal Univ, Coll Comp & Informat Technol, Xinxiang, Henan, Peoples R China
关键词
Adversarial Learning; Poisoning Attack; Robust Learning; Localized Generalization Error Model (L-GEM);
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Poisoning attack in which an adversary misleads the learning process by manipulating its training set significantly affect the performance of classifiers in security applications. This paper proposed a robust learning method which reduces the influences of attack samples on learning. The sensitivity, defined as the fluctuation of the output with small perturbation of the input, in Localized Generalization Error Model (L-GEM) is measured for each training sample. The classifier's output on attack samples may be sensitive and inaccurate since these samples are different from other untainted samples. An import score is assigned to each sample according to its localized generalization error bound. The classifier is trained using a new training set obtained by resampling the samples according to their importance scores. RBFNN is applied as the classifier in experimental evaluation. The proposed model outperforms than the traditional one under the well-known label flip poisoning attacks including nearest-first and farthest-first flips attack.
引用
收藏
页码:175 / 178
页数:4
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