[paper]One Pixel Attack for Fooling Deep Neural Networks
之前對抗攻擊算法都是在整個圖像的所有像素點上做微小的擾動，以達到欺騙模型的目的。而本文的思想是隻改變少量的像素點，甚至在只改變一個像素點的極端情況下就能獲得較好的攻擊效果。提出了一種基於差分進化（DE）生成單像素對抗樣本的黑盒攻擊（僅需要概率標籤）算法。由於DE的固有屬性，僅需要較少的對抗信息就可以欺騙更多類型的網絡。 算法優點 ： 高效性 半黑盒攻擊：只需要返回黑盒的類標概率而不用網絡的內部參數
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相關文章
- 1. One Pixel Attack for Fooling Deep Neural Networks
- 2. [最近成果] Patch-wise Attack for Fooling Deep Neural Network (ECCV2020)
- 3. Deep Neural Networks for Object Detection
- 4. Understanding Neural Networks Through Deep Visualization
- 5. Aggregated Residual Transformations for Deep Neural Networks
- 6. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 7. Improving Deep Neural Networks
- 8. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
- 9. Improving Deep Neural Networks [1]
- 10. Neural Networks and Deep Learning