ROBUSTNESS TO ADVERSARIAL EXAMPLES THROUGH AN ENSEMBLE OF SPECIALISTS
1.利用FGSM方法得到模型的混淆矩陣： 根據混淆矩陣將數據分爲2K+1個子集，在每個子集上訓練分類器。 2.僞代碼如下： 2k個專家子集和一個訓練10分類的cnn ensemble 3.實驗考慮三種模型 單個CNN，簡單集成CNN，specialists +1，其中每個卷積網都是（32+32+64+全連接+softmax。 投票機制無法使用通才來激活相關專家，因爲通常這會遭到對手的欺騙。在算法1
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相關文章
- 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的閱讀筆記
- 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