Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

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Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks

目录概主要内容算法一些有趣的指标鲁棒性定义合格的抗干扰机制 Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, Ananthram Swami, Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks. 概本文提出一种distillation model, 能够免疫大部分的adversarial attacks,…

Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks

目录概主要内容 Mustafa A., Khan S., Hayat M., Goecke R., Shen J., Shao L., Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks, arXiv preprint arXiv:1904.00887, 2019. 代码概类似的, 是large margin的思想, 亮点是多层? 主要内容以下, \(f^l\)表示第\(l\)层的输入,…

Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient

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Universal adversarial perturbations

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Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! arXiv:2003.10399v2 [cs.CV] 23 Jul 2020 ECCV 2020 1 https://github.com/ssharmin/spikingNN-adversarial-attack Abstract 在最近对可信任的神经网络的探索中,我们提出了一个潜在的候选,即脉冲神经网络(SNN)之于对抗攻击的内在鲁棒性.在这项工作中,我们证明对CIFAR数据集上的深度VGG和ResNet结构,在基于梯度的攻击下,…

（转）Awesome Knowledge Distillation

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