[paper]DeepFool: a simple and accurate method to fool deep neural networks
本文目標是尋求最小的擾動來達到生成對抗樣本的目標，因此提出了DeepFool的算法來生成擾動，並且提出了一種量化分類器魯棒性的方法。 這是第一個通過計算出最小的必要擾動，並應用到對抗樣本構建的方法，使用的限制擾動規模的方法是L2範數。最終得到的對抗樣本效果要優於前面的FGSM和JSMA方法，但是這三者都需要比較大的計算資源。 根據樣本 x x x，標籤 k ^ ( x ) \hat{k}(x) k
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相關文章
- 1. DeepFool: a simple and accurate method to fool deep neural networks
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