[advGAN]Generating Adversarial Examples With Adversarial Networks
這周讀論文。。讀的是這篇反正。這個內容比較新，網上也沒啥有特別有價值的參考內容，把學習筆記發上來，希望能有一點點幫助 嗯似乎是提出了用GAN以解決神經網絡安全性的問題。。。 白盒攻擊 攻擊者能夠獲知機器學習所使用的算法，以及算法所使用的參數。攻擊者在產生對抗性攻擊數據的過程中能夠與機器學習的系統有所交互。 黑盒攻擊 攻擊者並不知道機器學習所使用的算法和參數，但攻擊者仍能與機器學習的系統有所交互．比
相關文章
- 1. Generating Adversarial Examples with Adversarial Networks
- 2. Generating Adversarial Examples with Adversarial Networks閱讀筆記
- 3. Paper Review: Adversarial Examples
- 4. SPATIALLY TRANSFORMED ADVERSARIAL EXAMPLES
- 5. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
- 6. Generating Adversarial Examples with Adversarial Networks 採用GAN的方法來生成對抗樣本
- 7. Explaining and Harnessing Adversarial Examples
- 8. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 9. Partial Transfer Learning with Selective Adversarial Networks
- 10. [paper]SPATIALLY TRANSFORMED ADVERSARIAL EXAMPLES
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相關文章
- 1. Generating Adversarial Examples with Adversarial Networks
- 2. Generating Adversarial Examples with Adversarial Networks閱讀筆記
- 3. Paper Review: Adversarial Examples
- 4. SPATIALLY TRANSFORMED ADVERSARIAL EXAMPLES
- 5. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
- 6. Generating Adversarial Examples with Adversarial Networks 採用GAN的方法來生成對抗樣本
- 7. Explaining and Harnessing Adversarial Examples
- 8. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 9. Partial Transfer Learning with Selective Adversarial Networks
- 10. [paper]SPATIALLY TRANSFORMED ADVERSARIAL EXAMPLES