[paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
本文提出了兩種特徵壓縮方法： 減少每個像素的顏色位深度 使用空間平滑來減少各個像素之間的差異 特徵壓縮通過將與原始空間中許多不同特徵向量相對應的樣本合併爲單個樣本，從而減少了對手可用的搜索空間。通過將DNN模型對原始輸入的預測與對壓縮輸入的預測進行比較，特徵壓縮可以很好的檢測出對抗樣本。本方法對於計算資源要求不高，可以與其他防禦措施互補，並且可以結合聯合檢測框架使用。 Our approach,
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相關文章
- 1. 關於EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples的理解
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- 3. Deep Validation: Toward Detecting Real-world Corner Cases for Deep Neural Networks
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- 8. Generating Adversarial Examples with Adversarial Networks閱讀筆記
- 9. 《Detecting Adversarial Examples through Image Transformation》和CW attack的閱讀筆記
- 10. Neural Networks and Deep Learning