论文阅读笔记：Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
2018-03-14 08:32
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论文阅读笔记：Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
要点
(universal adversarial perturbation)[1]3D物理对抗样本
对抗样本生成
Box-constrained L-BFGSminρminρ
Fast Gradient Sign Method (FGSM)
ρ=ϵsign(∇J(θ,Ic,l))ρ=ϵsign(∇J(θ,Ic,l))
one-shot 生成方式要求ϵϵ不能太小
Basic Iterative Method (BIM)
Ik+1=clip(Ik+αsign(∇J(θ,Ik,l))Ik+1=clip(Ik+αsign(∇J(θ,Ik,l))
Iterative Least-likely Class Method (ILCM)
选取测试概率最低的那个类作为target class进行BIM targeted adversarial example generation
Jacobian-based Saliency Map Attack (JSMA)
One Pixel Attack
Carlini and Wagner Attacks (C&W)
DeepFool
Universal Adversarial Perturbations
对几乎所有输入均有效、不依赖于输入的对抗扰动
UPSET(Universal Perturbations for Steering to Exact Targets)
ANGRI(Antagonistic Network for Generating Rogue Images)
对抗训练
参考文献
[1]
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