面向正常拟合迁移学习模型的成员推理攻击

陈晋音; 上官文昌; 张京京; 郑海斌; 郑雅羽; 张旭鸿

doi:10.11959/j.issn.1000-436x.2021209

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面向正常拟合迁移学习模型的成员推理攻击

学术论文 | 更新时间：2024-06-05

- 面向正常拟合迁移学习模型的成员推理攻击
- Membership inference attacks against transfer learning for generalized model
- 通信学报 2021年42卷第10期页码：197-210
- 作者机构：
  
  1. 浙江工业大学网络空间安全研究院，浙江杭州 310012
  2. 浙江工业大学信息工程学院，浙江杭州 310012
  3. 军事科学院系统工程研究院信息系统安全技术国防科技重点实验室，北京 100039
  4. 浙江大学控制科学与工程学院，浙江杭州 310007
- 作者简介：
  
  [ "陈晋音（1982- ），女，浙江宁波人，博士，浙江工业大学教授，主要研究方向为智能计算、数据挖掘、网络安全等" ]
  [ "上官文昌（1996- ），男，湖北十堰人，浙江工业大学硕士生，主要研究方向为深度学习、人工智能、深度学习、隐私攻防等" ]
  [ "张京京（1988- ），男，北京人，博士，军事科学院系统工程研究院工程师，主要研究方向为深度学习、人工智能和对抗性攻击和防御等" ]
  [ "郑海斌（1995- ），男，浙江台州人，浙江工业大学博士生，主要研究方向为深度学习、人工智能和对抗性攻击和防御等" ]
  [ "郑雅羽（1978- ），男，浙江温州人，博士，浙江工业大学副教授，主要研究方向为嵌入式软硬件应用开发、视频图像处理算法、服务器网络技术等" ]
  [ "张旭鸿（1988- ），男，河北石家庄人，博士，浙江大学助理教授，主要研究方向为分布式大数据与人工智能系统、大数据挖掘与分析、数据驱动安全、人工智能与安全等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018AAA0100801);国家自然科学基金资助项目(62072406);浙江省自然科学基金资助项目(LY19F020025);宁波市“科技创新2025”重大专项基金资助项目(2018B10063)
- DOI：10.11959/j.issn.1000-436x.2021209
  中图分类号： TN92
- 网络首发：2021-10，
  
  纸质出版：2021-10-25
- 稿件说明：
移动端阅览
陈晋音, 上官文昌, 张京京, 等. 面向正常拟合迁移学习模型的成员推理攻击[J]. 通信学报, 2021,42(10):197-210.

Jinyin CHEN, Wenchang SHANGGUAN, Jingjing ZHANG, et al. Membership inference attacks against transfer learning for generalized model[J]. Journal on Communications, 2021, 42(10): 197-210.
陈晋音, 上官文昌, 张京京, 等. 面向正常拟合迁移学习模型的成员推理攻击[J]. 通信学报, 2021,42(10):197-210. DOI： 10.11959/j.issn.1000-436x.2021209.

Jinyin CHEN, Wenchang SHANGGUAN, Jingjing ZHANG, et al. Membership inference attacks against transfer learning for generalized model[J]. Journal on Communications, 2021, 42(10): 197-210. DOI： 10.11959/j.issn.1000-436x.2021209.

摘要

针对现有成员推理攻击（MIA）在面向正常拟合迁移学习模型时性能较差的问题，对迁移学习模型在正常拟合情况下的 MIA 进行了系统的研究，设计异常样本检测获取容易受攻击的数据样本，实现对单个样本的成员推理攻击。最终，将提出的攻击方法在 4 种图像数据集上展开攻击验证，结果表明，所提 MIA 有较好的攻击性能。例如，从VGG16（用Caltech101预训练）迁移的Flowers102分类器上，所提MIA实现了83.15%的成员推理精确率，揭示了在迁移学习环境下，即使不访问教师模型，通过访问学生模型依然能实现对教师模型的MIA。

Abstract

For the problem of poor performance of exciting membership inference attack (MIA) when facing the transfer learning model that is generalized

the MIA for the transfer learning model that is generalized was first systematically studied

the anomaly detection was designed to obtain vulnerable data samples

and MIA was carried out against individual samples.Finally

the proposed method was tested on four image data sets

which shows that the proposed MIA has great attack performance.For example

on the Flowers102 classifier migrated from VGG16 (pretraining with Caltech101)

the proposed MIA achieves 83.15% precision

which reveals that in the environment of transfer learning

even without access to the teacher model

the MIA for the teacher model can be achieved by visiting the student model.

关键词

Keywords

references

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