關於Adversarial Machine Learning at Scale的理解
Goodfellow基於之前的FGSM攻擊方法做出了一部分改進。鑑於之前的FGSM的成功率並不高（在imageNet上僅有 63%−69% ）。Goodfellow做出了一些改進，從原先的以增加原始類別標記的損失函數爲目標變爲了減少目標類別的損失函數爲目標： Xadv=X−ϵsign(∇XJ(X,ytarget)) 其中 ytarget 是所有類別標記中"最不可能"的標記。即 yLL=argmi
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相關文章
- 1. [paper]ADVERSARIAL MACHINE LEARNING AT SCALE
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