EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES

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《Explaining and harnessing adversarial examples》论文学习报告

<Explaining and harnessing adversarial examples> 论文学习报告组员:裴建新赖妍菱周子玉 2020-03-27 1 背景 Szegedy有一个有趣的发现:有几种机器学习模型,包括最先进的神经网络,很容易遇到对抗性的例子.所谓的对抗性样例就是对数据集中的数据添加一个很小的扰动而形成的输入.在许多情况下,在训练数据的不同子集上训练不同体系结构的各种各样的模型错误地分类了相同的对抗性示例.这表明,对抗性例子暴露了我们训练算法中的基本盲点.…

EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES

目录概主要内容从线性谈起非线性 Goodfellow I, Shlens J, Szegedy C, et al. Explaining and Harnessing Adversarial Examples[J]. arXiv: Machine Learning, 2014. @article{goodfellow2014explaining, title={Explaining and Harnessing Adversarial Examples}, author={Goodfel…

Adversarial Examples for Semantic Segmentation and Object Detection 阅读笔记

Adversarial Examples for Semantic Segmentation and Object Detection (语义分割和目标检测中的对抗样本) 作者:Cihang Xie, Jianyu Wang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, Alan Yuille, Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218 U…

文本adversarial examples

对文本对抗性样本的研究极少,近期论文归纳如下: 文本对抗三个难点: text data是离散数据,multimedia data是连续数据,样本空间不一样: 对text data的改动可能导致数据不合法: 基于word的改动(替换.增.删)会有语义兼容性的问题: 论文: Deep Text Classification Can be Fooled 和 Towards Crafting Text Adversarial Samples: 针对文本分类生成对抗样本——对输入文本进行增删改处理,使得文…

论文阅读 | Generating Fluent Adversarial Examples for Natural Languages

Generating Fluent Adversarial Examples for Natural Languages ACL 2019 为自然语言生成流畅的对抗样本摘要有效地构建自然语言处理(NLP)任务的对抗性攻击者是一个真正的挑战.首先,由于句子空间是离散的.沿梯度方向做小扰动是困难的.其次,生成的样本的流畅性不能保证.在本文中,我们提出了MHA,它通过执行Metropolis-Hastings抽样来解决这两个问题,其建议是在梯度的指导下设计的.在IMDB和SNLI上的实验表明,…

Limitations of the Lipschitz constant as a defense against adversarial examples

目录概主要内容 Huster T., Chiang C. J. and Chadha R. Limitations of the lipschitz constant as a defense against adversarial examples. In European Conference on Machine Learning and Data Mining (ECML PKDD), 2018. 概本文是想说明现有的依赖Lipschitz常数的以获得可验证的鲁棒性存在很大局限性.…

Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples

Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples 目录概主要内容实验设置损失的影响额外的数据网络结构其他的一些tricks Gowal S., Qin C., Uesato J., Mann T. & Kohli P. Uncovering the Limits of Adversarial Training against Norm-Bounded Adv…

Certified Robustness to Adversarial Examples with Differential Privacy

目录概主要内容 Differential Privacy insensitivity Lemma1 Proposition1 如何令网络为-DP in practice Lecuyer M, Atlidakis V, Geambasu R, et al. Certified Robustness to Adversarial Examples with Differential Privacy[C]. ieee symposium on security and privacy, 2019:…

Generating Adversarial Examples with Adversarial Networks

目录概主要内容 black-box 拓展 Xiao C, Li B, Zhu J, et al. Generating Adversarial Examples with Adversarial Networks[J]. arXiv: Cryptography and Security, 2018. @article{xiao2018generating, title={Generating Adversarial Examples with Adversarial Networks}, a…

Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

目录概主要内容 Obfuscated Gradients BPDA 特例一般情形 EOT Reparameterization 具体的案例 Thermometer encoding Input transformations LID Stochastic Activation Pruning Mitigating through randomization PixelDefend DenfenseGAN Athalye A, Carlini N, Wagner D, et al. Obfu…