論文筆記：ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learn
這篇文章是上一篇Membership Inference Attacks Against Machine Learning Models的跟蹤研究，其提出了三個攻擊模型，將上一篇中的模型一步步簡化。 第一個攻擊模型是將上一篇中的影子模型數量降到一個，發現實驗效果不會有啥下降。 第二個模型放寬攻擊者對目標模型訓練集的限制，認爲攻擊者可以對其一無所知。影子模型的訓練集採用的是和目標模型不同領域的數據集
相關文章
- 1. 論文筆記：Membership Inference Attacks Against Machine Learning Models
- 2. 論文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）
- 3. 對抗樣本(論文解讀四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
- 4. Classification and inference with machine learning
- 5. 論文學習筆記 MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- 6. Federated Machine Learning:Concept and Applications(論文筆記)
- 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. 論文筆記：Teaching Machine to Read and Comprehend
- 更多相關文章...
- • SQLite AND/OR 運算符 - SQLite教程
- • SQL AND & OR 運算符 - SQL 教程
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • RxJava操作符（七）Conditional and Boolean
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 論文筆記：Membership Inference Attacks Against Machine Learning Models
- 2. 論文解析：Membership Inference Attacks Against Machine Learning Models（一看即懂）
- 3. 對抗樣本(論文解讀四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
- 4. Classification and inference with machine learning
- 5. 論文學習筆記 MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
- 6. Federated Machine Learning:Concept and Applications(論文筆記)
- 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. 論文筆記：Teaching Machine to Read and Comprehend