關於EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples的理解
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
- 8. Generating Adversarial Examples with Adversarial Networks
- 9. Adversarial Examples 對抗樣本
- 10. Recognizing irregular entities in biomedical text via deep neural networks