关于EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples的理解

时间 2020-12-27

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在本文中，作者基于之前的Carlini & Wagner攻击提出了一些新的改进，从而在确保攻击成功率的情况下，增强了攻击的可转移性。作者仍然沿用之前C&W攻击的目标函数 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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1. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
2. 关于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. 【生成对抗样本】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
8. Generating Adversarial Examples with Adversarial Networks
9. Adversarial Examples 对抗样本
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1. [paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
2. 关于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. 【生成对抗样本】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
8. Generating Adversarial Examples with Adversarial Networks
9. Adversarial Examples 对抗样本
10. Recognizing irregular entities in biomedical text via deep neural networks

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