1.FGSM
单步攻击,fast gradient sign method对抗样本生成方法,通过更新对抗扰动,增大图片分类损失,将样本推过分类决策边界。
对抗扰动更新方法如下
X a d v = X + ϵ × s i g n ( ∇ X L ( X , y t r u e ; θ ) ) X^{\mathbf{adv}} = X + \epsilon \times \mathbf{sign}\big(\nabla_{X}L(X,y^{\mathbf{true}}; \theta)\big) Xadv=X+ϵ×sign(∇XL(X,ytrue;θ))
2.I_FGSM
对单步攻击FGSM进行迭代,扰动更新方法如下
X 0 a d v = X ; X n + 1 a d v = C l i p X ϵ { X n a d v + α × s i g n ( ∇ X L ( X n a d v , y t r u e ; θ ) ) } X^{\mathbf{adv}}_{0} = X; \\ \\ X^{\mathbf{adv}}_{n+1} = \mathbf{Clip}^{\epsilon}_X \{X^{\mathbf{adv}}_n + \alpha \times \mathbf{sign}\big(\nabla_{X}L(X^{\mathbf{adv}}_n,y^{\mathbf{true}}; \theta)\big) \} X0adv=X;Xn+1adv=ClipXϵ{
Xnadv+α×sign(∇XL(Xnad