論文解讀《Boosting Adversarial Attacks with Momentum》
摘要 我們提出了一種廣泛的基於動量的迭代算法來增強對抗攻擊。通過將動量項集成到迭代過程中，我們的方法可以在迭代過程中穩定更新方向並避免糟糕的局部最大值 1.FGSM 我們先來了解一下迭代的FGSM算法,它通過以下公式來產生擾動 x ∗ x_* x∗ x ∗ = x + ϵ ∗ s i g n ( ∇ x J ( x ∗ , y ) ) ， ( 1 ) x_*=x+{\epsilon}*sign(
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- 1. [paper]Boosting Adversarial Attacks with Momentum
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- 7. MIXUP INFERENCE: BETTER EXPLOITING MIXUP TO DEFEND ADVERSARIAL ATTACKS 論文閱讀
- 8. 對抗樣本(論文解讀二): Transferable Adversarial Attacks for Image and Video Object Detection
- 9. 對抗樣本(論文解讀四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
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相關文章
- 1. [paper]Boosting Adversarial Attacks with Momentum
- 2. 【遷移攻擊論文筆記】動量邏輯集成！MI-FGSM！Boosting Adversarial Attacks with Momentum
- 3. 論文閱讀 Decision-based Black-box Adversarial Attacks
- 4. [論文解讀]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 5. Boosting Adversarial Training with Hypersphere Embedding
- 6. Adversarial Network Embedding論文解讀
- 7. MIXUP INFERENCE: BETTER EXPLOITING MIXUP TO DEFEND ADVERSARIAL ATTACKS 論文閱讀
- 8. 對抗樣本(論文解讀二): Transferable Adversarial Attacks for Image and Video Object Detection
- 9. 對抗樣本(論文解讀四): Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
- 10. 對抗樣本(論文解讀七):On Physical Adversarial Patches for Object Detection