[paper]Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
本文提出了一種防禦方法，即由高級特徵主導的去噪器 high-level representation guided denoiser(HGD)。 標準去噪器具有誤差放大效應，在這種效應中，較小的殘留對抗噪聲影響會逐漸放大，最終導致錯誤的分類。HGD通過將損失函數定義爲由乾淨圖像和去噪圖像激活的目標模型輸出之間的差值解決了這個問題。 HGD具有三個優點： HGD可以使目標模型面對對抗攻擊更加魯棒 H
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相關文章
- 1. Practical Black-Box Attacks against Machine Learning
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