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關於EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples的理解
時間 2020-12-27
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在本文中,作者基於之前的Carlini & Wagner攻擊提出了一些新的改進,從而在確保攻擊成功率的情況下,增強了攻擊的可轉移性。 作者仍然沿用之前C&W攻擊的目標函數 f(x,t) f ( x , t ) : f(x,t)=max{maxj≠t[Logit(x)]j−[Logit(x)]t,−k} f ( x , t ) = max { max j ≠ t [ L o g i t ( x
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相關文章
1.
[paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
2.
關於Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks的理解
3.
Paper Review: Adversarial Examples
4.
[advGAN]Generating Adversarial Examples With Adversarial Networks
5.
【生成對抗樣本】Simple Black-Box Adversarial Attacks on Deep Neural Networks
6.
PGD:Towards Deep Learning Models Resistant to Adversarial Attacks
7.
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
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