ROBUSTNESS TO ADVERSARIAL EXAMPLES THROUGH AN ENSEMBLE OF SPECIALISTS
1.利用FGSM方法得到模型的混淆矩阵： 根据混淆矩阵将数据分为2K+1个子集，在每个子集上训练分类器。 2.伪代码如下： 2k个专家子集和一个训练10分类的cnn ensemble 3.实验考虑三种模型 单个CNN，简单集成CNN，specialists +1，其中每个卷积网都是（32+32+64+全连接+softmax。 投票机制无法使用通才来激活相关专家，因为通常这会遭到对手的欺骗。在算法1
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相关文章
- 1. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
- 2. Certified Robustness to Adversarial Examples with Differential Privacy
- 3. [paper]IMPROVING ADVERSARIAL ROBUSTNESS REQUIRES REVISITING MISCLASSIFIED EXAMPLES
- 4. Paper Review: Adversarial Examples
- 5. 《Detecting Adversarial Examples through Image Transformation》和CW attack的阅读笔记
- 6. Explaining and Harnessing Adversarial Examples
- 7. SPATIALLY TRANSFORMED ADVERSARIAL EXAMPLES
- 8. [advGAN]Generating Adversarial Examples With Adversarial Networks
- 9. Generating Adversarial Examples with Adversarial Networks
- 10. Certified Adversarial Robustness via Randomized Smoothing