《Detecting Adversarial Examples through Image Transformation》和CW attack的阅读笔记

时间 2021-01-02

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本文关于《Detecting Adversarial Examples through Image Transformation》和《Towards Evaluating the Robustness of Neural Networks》，因为文献阅读顺序，我先从《Detecting Adversarial Examples through Image Transformation》来展开，并补

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1. Generating Adversarial Examples with Adversarial Networks阅读笔记
2. 论文阅读笔记：GENERATING NATURAL ADVERSARIAL EXAMPLES
3. Paper Review: Adversarial Examples
4. 【阅读笔记】Generative Adversarial Nets
5. 《Who’s Afraid of Adversarial Queries The Impact of Image》文献阅读笔记
6. Data-Free Adversarial Perturbations for Practical Black-Box Attack阅读笔记
7. Image Transformation can make Neural Networks more robust against Adversarial Examples
8. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
9. Explaining and Harnessing Adversarial Examples 论文阅读
10. 论文笔记--CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation
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1. Generating Adversarial Examples with Adversarial Networks阅读笔记
2. 论文阅读笔记：GENERATING NATURAL ADVERSARIAL EXAMPLES
3. Paper Review: Adversarial Examples
4. 【阅读笔记】Generative Adversarial Nets
5. 《Who’s Afraid of Adversarial Queries The Impact of Image》文献阅读笔记
6. Data-Free Adversarial Perturbations for Practical Black-Box Attack阅读笔记
7. Image Transformation can make Neural Networks more robust against Adversarial Examples
8. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
9. Explaining and Harnessing Adversarial Examples 论文阅读
10. 论文笔记--CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image Transformation

>>更多相关文章<<