【迁移攻击论文笔记】动量逻辑集成！MI-FGSM！Boosting Adversarial Attacks with Momentum
1.更新策略选择 ①optimization-based和多步方法IGSM：成功率高；转移性低。（可能“过拟合”or陷入局部最优） ②单步方法FGSM： 成功率低；转移性高。 所以用: MI-FGSM： 2.集成方式选择 MI-FGSM还不够，得利用集成网络来进一步提高成功率。利用的方式有3种待测试： ①输入softmax的logits ②softmax输出的prediciton ③计算得到的lo
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相关文章
- 1. 论文解读《Boosting Adversarial Attacks with Momentum》
- 2. [paper]Boosting Adversarial Attacks with Momentum
- 3. 【迁移攻击笔记】梯度平滑攻击！UNDERSTANDING AND ENHANCING THE TRANSFERABILITY OF ADVERSARIAL EXAMPLES
- 4. 【迁移攻击笔记】图像＆模型同时迁移2017Universal adversarial perturbations
- 5. 【迁移攻击笔记】2019.11分批次集成攻击A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories
- 6. 【论文笔记】CatBoost: unbiased boosting with categorical features
- 7. Object Region Mining with Adversarial Erasing论文笔记
- 8. [advGAN]Generating Adversarial Examples With Adversarial Networks
- 9. [读论文]通过中间层攻击提高对抗样本的迁移性(Enhancing Adversarial Example Transferability with an ILA)
- 10. 论文阅读 Decision-based Black-box Adversarial Attacks