[paper]One Pixel Attack for Fooling Deep Neural Networks
之前对抗攻击算法都是在整个图像的所有像素点上做微小的扰动，以达到欺骗模型的目的。而本文的思想是只改变少量的像素点，甚至在只改变一个像素点的极端情况下就能获得较好的攻击效果。提出了一种基于差分进化（DE）生成单像素对抗样本的黑盒攻击（仅需要概率标签）算法。由于DE的固有属性，仅需要较少的对抗信息就可以欺骗更多类型的网络。 算法优点 ： 高效性 半黑盒攻击：只需要返回黑盒的类标概率而不用网络的内部参数
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相关文章
- 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