论文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）

时间 2021-03-07

标签论文笔记神经网络机器学习繁體版

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论文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂，超详细版本）摘要：这篇文章致力于探索机器学习模型如何泄露训练集中的信息，专注于基本的成员推理攻击，即给出一个机器学习模型和一条记录，判断该样本是否被用作训练集中的一部分。我们对“机器学习即服务（machine learning as a service)

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1. 论文笔记：Membership Inference Attacks Against Machine Learning Models
2. 论文学习笔记 MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
3. Practical Black-Box Attacks against Machine Learning
4. 论文笔记：ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learn
5. 论文解析：Machine Learning with Membership Privacy using Adversarial Regularization
6. [paper]Practical Black-Box Attacks against Machine Learning
7. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
8. Practical Black-Box Attacks against Machine Learning 阅读笔记
9. Classification and inference with machine learning
10. PGD:Towards Deep Learning Models Resistant to Adversarial Attacks
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1. 论文笔记：Membership Inference Attacks Against Machine Learning Models
2. 论文学习笔记 MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
3. Practical Black-Box Attacks against Machine Learning
4. 论文笔记：ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learn
5. 论文解析：Machine Learning with Membership Privacy using Adversarial Regularization
6. [paper]Practical Black-Box Attacks against Machine Learning
7. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
8. Practical Black-Box Attacks against Machine Learning 阅读笔记
9. Classification and inference with machine learning
10. PGD:Towards Deep Learning Models Resistant to Adversarial Attacks

>>更多相关文章<<