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

【DEFENSE-GAN: PROTECTING CLASSIFIERS AGAINST ADVERSARIAL ATTACKS USING GENERATIVE MODELS】的更多相关文章

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…

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…

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…

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.},…

AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets

目录概主要内容符号说明 Original Generator Transfer the Generator Wang X., He K., Guo C., Weinberger K., Hopcroft H., AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets. arXiv preprint, arXiv: 1904.00783, 2019. 概用GA…

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…

KDD 2019放榜！录取率仅14%，强调可重现性

[导读]KDD 2019录取结果终于放榜了,今年Research和ADS两个 track共评审论文1900篇,其中Research track的录取率只有14%.今年也是KDD第一次采用双盲评审政策,并强调提交论文可重现内容.因此,论文质量特别值得期待. KDD 2019录取论文终于放榜了!你的论文"中奖"了吗? ACM SIGKDD(国际数据挖掘与知识发现大会,简称 KDD)是世界数据挖掘领域的最高级别的学术会议,由 ACM 的数据挖掘及知识发现专委会(SIGKDD)主办,被中国计算…

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,…

论文解读（GAN）《Generative Adversarial Networks》

Paper Information Title:<Generative Adversarial Networks>Authors:Ian J. Goodfellow, Jean Pouget-Abadie, M. Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua BengioSources:2014, NIPSOther:26700 Citations, 41 ReferencesCode…

Generative Adversarial Nets[Wasserstein GAN]

本文来自<Wasserstein GAN>,时间线为2017年1月,本文可以算得上是GAN发展的一个里程碑文献了,其解决了以往GAN训练困难,结果不稳定等问题. 1 引言本文主要思考的是半监督学习.当我们说学习概率分布,典型的思维是学习一个概率密度.这通常是通过定义一个概率密度的参数化族$(P_{\theta})_{\theta\in R^d}$,然后基于样本最大似然:如果当前有真实样本$\{x^{(i)}\}_{i=1}^m$,那么是问题转换成: \[\underset{\thet…