Defending Adversarial Attacks by Correcting logits

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Defending Adversarial Attacks by Correcting logits

目录概主要内容实验 Li Y., Xie L., Zhang Y., Zhang R., Wang Y., Tian Q., Defending Adversarial Attacks by Correcting logits[J]. arXiv: Learning, 2019. 概作者认为, adversarial samples 和 natural samples的分布是不同, 结果二者的输出logits的分布也是不同的, 那么能否通过此来还原正确的类别呢? 主要内容思路是这样子的…

Mind the Box: $\ell_1$-APGD for Sparse Adversarial Attacks on Image Classifiers

目录概主要内容 Croce F. and Hein M. Mind the box: $\ell_1$-APGD for sparse adversarial attacks on image classifiers. In International Conference on Machine Learning (ICML), 2021. 概以往的$\ell_1$攻击, 为了保证 \[\|x' - x\|_1 \le \epsilon, x' \in [0, 1]^d, \] 其…

DEFENSE-GAN: PROTECTING CLASSIFIERS AGAINST ADVERSARIAL ATTACKS USING GENERATIVE MODELS

目录概主要内容 Samangouei P, Kabkab M, Chellappa R, et al. Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models.[J]. arXiv: Computer Vision and Pattern Recognition, 2018. @article{samangouei2018defense-gan:, title={Defen…

Towards Deep Learning Models Resistant to Adversarial Attacks

目录概主要内容 Note Madry A, Makelov A, Schmidt L, et al. Towards Deep Learning Models Resistant to Adversarial Attacks.[J]. arXiv: Machine Learning, 2017. @article{madry2017towards, title={Towards Deep Learning Models Resistant to Adversarial Attacks.},…

论文阅读 | Real-Time Adversarial Attacks

摘要以前的对抗攻击关注于静态输入,这些方法对流输入的目标模型并不适用.攻击者只能通过观察过去样本点在剩余样本点中添加扰动. 这篇文章提出了针对于具有流输入的机器学习模型的实时对抗攻击. 1 介绍在实时处理场景中,攻击者只能观察数据样本的过去部分,并且只能向数据样本的未来部分添加扰动,而目标模型的决策将基于整个数据样本. 当攻击实时系统时,攻击者面临着观察空间和操作空间之间的权衡.也就是说,假设目标系统接受顺序输入x,攻击者可以选择在开始时设计对抗性扰动.然而,在这种情况下,攻击者对x没有任何…

Attacks for RL

1. http://rll.berkeley.edu/adversarial/ Adversarial Attacks on Neural Network Policies 就是对test时候的policy进行构造对抗样本,方法还是用的分类对抗样本的方法,对于DQN,把Q value做个softmax,一样地构造对抗样本.…

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

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:2001.01587v1 [cs.NE] 1 Jan 2020 Abstract 脉冲神经网络(SNN)被广泛应用于神经形态设备中,以模拟大脑功能.在这种背景下,SNN的安全性变得重要但缺乏深入的研究,这与深度学习的热潮不同.为此,我们针对SNN的对抗攻击,确认了与ANN攻击不同的几个挑战:i)当前的对抗攻击是基于SNN中以时空模式呈现的梯度信息,这在传统的学习算法中很难获得:ii)在梯度累积过程中,输入的连续梯度与二值脉…

Adversarial Detection methods

目录 Kernel Density (KD) Local Intrinsic Dimensionality (LID) Gaussian Discriminant Analysis (GDA) Gaussian Mixture Model (GMM) SelectiveNet Combined Abstention Robustness Learning (CARL) Adversarial Training with a Rejection Option Energy-based Out-of…

Adversarial Examples Are Not Bugs, They Are Features

目录概主要内容符号说明及部分定义可用特征稳定可用特征可用不稳定特征标准(standard)训练稳定(robust)训练分离出稳定数据分离出不稳定数据随机选取选取依赖于比较重要的实验 1 迁移性理论分析定理1 定理2 定理3 Ilyas A, Santurkar S, Tsipras D, et al. Adversarial Examples Are Not Bugs, They Are Features[C]. neural information process…

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,…