[paper]Boosting Adversarial Attacks with Momentum
本文提出一个基于动量(Momentum)的迭代算法，该方法通过梯度以迭代的方式对输入进行扰动以最大化损失函数，并且该方法还会在迭代过程中沿损失函数的梯度方向累加速度矢量，目的是稳定更新方向并避免糟糕的局部最大值。从而产生更好的可迁移(transferable)的对抗样本，解决了对抗样本生成算法对于黑盒模型的低成功率问题。 文中提到： 对抗样本迁移性的现象是由于不同的机器学习模型在数据点周围学习到相
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相关文章
- 1. 论文解读《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. 【迁移攻击论文笔记】动量逻辑集成！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. 论文阅读 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