PGD:Towards Deep Learning Models Resistant to Adversarial Attacks
一.摘要 1.出發點：（1）深度網絡對對抗攻擊（adversarial attack）即對對抗樣本的天生缺陷。 （2）當輸入信息與自然樣本差別並不大是卻被網絡錯誤的分類，如下圖。 2.作者提出方法和論證如下： （1）從魯棒優化的角度研究了神經網絡的抗差魯棒性。這與很多最近的相關工作具有廣泛的一致性 eg：防禦蒸餾，特徵壓縮。 （2）根據方法的原理性使得該方法在普遍範圍內是通用和有效的，即使用最強的
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相關文章
- 1. 對抗機器學習——Min Max模型(Towards Deep Learning Models Resistant to Adversarial Attacks)
- 2. [論文解讀]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 3. [轉載][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 4. Model-Reuse Attacks on Deep Learning Systems
- 5. 關於EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples的理解
- 6. Deep Learning Models on Kubernetes with GPUs
- 7. Deploying deep learning models in OpenCV
- 8. 【對抗攻擊】【綜述】Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 9. How To Improve Deep Learning Performance
- 10. WHEN NOT TO USE DEEP LEARNING