[paper]Boosting Adversarial Attacks with Momentum
本文提出一個基於動量(Momentum)的迭代算法，該方法通過梯度以迭代的方式對輸入進行擾動以最大化損失函數，並且該方法還會在迭代過程中沿損失函數的梯度方向累加速度矢量，目的是穩定更新方向並避免糟糕的局部最大值。從而產生更好的可遷移(transferable)的對抗樣本，解決了對抗樣本生成算法對於黑盒模型的低成功率問題。 文中提到： 對抗樣本遷移性的現象是由於不同的機器學習模型在數據點周圍學習到相
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相關文章
- 1. 論文解讀《Boosting Adversarial Attacks with Momentum》
- 2. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
- 3. [advGAN]Generating Adversarial Examples With Adversarial Networks
- 4. 【遷移攻擊論文筆記】動量邏輯集成！MI-FGSM！Boosting Adversarial Attacks with Momentum
- 5. ENHANCING TRANSFORMATION-BASED DEFENSES AGAINST ADVERSARIAL ATTACKS WITH A DISTRIBUTION CLASSIFIER
- 6. Generating Adversarial Examples with Adversarial Networks
- 7. 論文閱讀 Decision-based Black-box Adversarial Attacks
- 8. PGD:Towards Deep Learning Models Resistant to Adversarial Attacks
- 9. DoS Attacks Prevention with TCP Intercept
- 10. Improved Baselines with Momentum Contrastive Learning