[paper]Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
本文提出了两种特征压缩方法： 减少每个像素的颜色位深度 使用空间平滑来减少各个像素之间的差异 特征压缩通过将与原始空间中许多不同特征向量相对应的样本合并为单个样本，从而减少了对手可用的搜索空间。通过将DNN模型对原始输入的预测与对压缩输入的预测进行比较，特征压缩可以很好的检测出对抗样本。本方法对于计算资源要求不高，可以与其他防御措施互补，并且可以结合联合检测框架使用。 Our approach,
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相关文章
- 1. 关于EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples的理解
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