The Limitations of Deep Learning in Adversarial Settings論文筆記
0.摘要 基於對DNNs輸入輸出映射的精確理解，提出了JSMA算法構建對抗樣本，在實驗中取得優異效果，最後描述了通過定義一個良性輸入和目標分類之間的距離的預測度量來防範對抗樣本的初步工作。 1.介紹 1.深度學習應用廣泛 2.舉例：實際生活中對抗樣本的危害 3.舉例：對抗樣本如何發揮作用 4.闡明本文工作 5.數學形式定義需解決問題 JSMA爲目標攻擊，輸入X加上擾動後，神經網絡將其歸類爲Y*，我
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相關文章
- 1. [paper]The Limitations of Deep Learning in Adversarial Settings(JSMA）
- 2. The Limitations of Deep Learning in Adversarial Settings
- 3. 【論文回顧】The Limitations of Deep Learning in Adversarial Settings
- 4. 關於The Limitations of Deep Learning in Adversarial Settings的理解
- 5. 對The Limitations of Deep Learning in Adversarial Settings理解
- 6. Deep Learning 論文筆記 (3): Deep Learning Face Attributes in the Wild
- 7. [論文解讀]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 8. 論文閱讀筆記《Deep Meta-Learning: Learning to Learn in the Concept Space》
- 9. 【論文筆記】Deep Learning on Graphs: A Survey
- 10. Machine Learning & Deep Learning 論文閱讀筆記