关于Adversarial Machine Learning at Scale的理解
Goodfellow基于之前的FGSM攻击方法做出了一部分改进。鉴于之前的FGSM的成功率并不高（在imageNet上仅有 63%−69% ）。Goodfellow做出了一些改进，从原先的以增加原始类别标记的损失函数为目标变为了减少目标类别的损失函数为目标： Xadv=X−ϵsign(∇XJ(X,ytarget)) 其中 ytarget 是所有类别标记中"最不可能"的标记。即 yLL=argmi
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