论文笔记：ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learn
这篇文章是上一篇Membership Inference Attacks Against Machine Learning Models的跟踪研究，其提出了三个攻击模型，将上一篇中的模型一步步简化。 第一个攻击模型是将上一篇中的影子模型数量降到一个，发现实验效果不会有啥下降。 第二个模型放宽攻击者对目标模型训练集的限制，认为攻击者可以对其一无所知。影子模型的训练集采用的是和目标模型不同领域的数据集
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相关文章
- 1. 论文笔记：Membership Inference Attacks Against Machine Learning Models
- 2. 论文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）
- 3. 对抗样本(论文解读四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
- 4. Classification and inference with machine learning
- 5. 论文学习笔记 MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- 6. Federated Machine Learning:Concept and Applications(论文笔记)
- 7. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- 8. Stronger Data Poisoning Attacks Break Data Sanitization Defenses
- 9. Machine Learning 05 - Model Evaluation and Analysis
- 10. 论文笔记：Teaching Machine to Read and Comprehend