【遷移攻擊論文筆記】動量邏輯集成！MI-FGSM！Boosting Adversarial Attacks with Momentum
1.更新策略選擇 ①optimization-based和多步方法IGSM：成功率高；轉移性低。（可能「過擬合」or陷入局部最優） ②單步方法FGSM： 成功率低；轉移性高。 所以用: MI-FGSM： 2.集成方式選擇 MI-FGSM還不夠，得利用集成網絡來進一步提高成功率。利用的方式有3種待測試： ①輸入softmax的logits ②softmax輸出的prediciton ③計算得到的lo
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相關文章
- 1. 論文解讀《Boosting Adversarial Attacks with Momentum》
- 2. [paper]Boosting Adversarial Attacks with Momentum
- 3. 【遷移攻擊筆記】梯度平滑攻擊！UNDERSTANDING AND ENHANCING THE TRANSFERABILITY OF ADVERSARIAL EXAMPLES
- 4. 【遷移攻擊筆記】圖像＆模型同時遷移2017Universal adversarial perturbations
- 5. 【遷移攻擊筆記】2019.11分批次集成攻擊A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories
- 6. 【論文筆記】CatBoost: unbiased boosting with categorical features
- 7. Object Region Mining with Adversarial Erasing論文筆記
- 8. [advGAN]Generating Adversarial Examples With Adversarial Networks
- 9. [讀論文]通過中間層攻擊提高對抗樣本的遷移性(Enhancing Adversarial Example Transferability with an ILA)
- 10. 論文閱讀 Decision-based Black-box Adversarial Attacks