[paper]Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
本文提出了一种防御方法，即由高级特征主导的去噪器 high-level representation guided denoiser(HGD)。 标准去噪器具有误差放大效应，在这种效应中，较小的残留对抗噪声影响会逐渐放大，最终导致错误的分类。HGD通过将损失函数定义为由干净图像和去噪图像激活的目标模型输出之间的差值解决了这个问题。 HGD具有三个优点： HGD可以使目标模型面对对抗攻击更加鲁棒 H
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相关文章
- 1. Practical Black-Box Attacks against Machine Learning
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