[论文总结] Adversarial Examples Are Not Bugs, They Are Features说在前面 NIPS 2019,原文链接:http://arxiv.org/abs/1905.02175;之前写过一个这篇文章的论文笔记,但是由于当时水平欠缺,还看不太懂其含义,…
【自娱自阅】There Are Many Consistent Explanations Of Unlabeled Data, should average 121 -- 21:30 App 【自娱自阅】Likelihood Landscapes A Unify Principle of many Adversarial Defenses 802 -- 27:08 App 【自娱自阅】Unsupervised Feature Learning via Non-Parametric Instance Discrimination浏览...
The paper "Adversarial Examples Are Not Bugs, They Are Features" from NIPS 2019, arxiv.org/abs/1905.0217... challenges conventional views on the vulnerability of deep neural networks. It proposes that adversarial examples are not anomalies, but rather an inherent aspect of the learni...
5月7日,MIT Madry组发布了一篇文章Adversarial Examples Are Not Bugs, They are features. 这篇文章试图解释为什么会存在Adversarial Examples,并得出结论,模型之所以会受到adversarial attack是因为它学习到了原始数据中的Non-robust but predictive 的特征。 自从Adversarial examples 被发现以来,关于它的研究就一直没有...
title={Adversarial Examples Are Not Bugs, They Are Features}, author={Ilyas, Andrew and Santurkar, Shibani and Tsipras, Dimitris and Engstrom, Logan and Tran, Brandon and Madry, Aleksander}, pages={125--136}, year={2019}}概作者认为, 标准训练方法, 由于既能学到稳定的特征和不稳定的特征, ...
目前有许多研究,但该问题还有很多可以研究的空间。一个观点是论文“Adversarial Examples Are Not Bugs, They Are Features”(https://arxiv.org/abs/1905.02175)所讨论的,论文中认为攻击成功的原因可能不是出现在模型上,而是出现在输入的资料上。 我们希望attack signal越小越好。在文献上有人成功做出one pixel ...
A Discussion of ‘adversarial examples are not bugs, they are features’: learning from incorrectly labeled data. Distill 4, e00019.6 (2019). Article Google Scholar Goodman, N. Fact, Fiction, and Forecast (Harvard Univ. Press, 1983). Quine, W. V. in Essays in Honor of Carl G. Hempel...
Ilyas, A. et al. Adversarial examples are not bugs, they are features. Preprint at:https://arxiv.org/abs/1905.02175(2019). Xie, C. & Yuille, A. Intriguing properties of adversarial training at scale.International Conference on Learning Representationshttps://openreview.net/forum?id=HyxJhCEFDS...
[19] GILMER J, METZ L, FAGHRI F等. Adversarial spheres[C]//6th International Conference on Learning Representations, ICLR 2018 - Workshop Track Proceedings. 2018. [20] ILYAS A, SANTURKAR S, TSIPRAS D等. Adversarial Examples Are Not Bugs, They Are Features[J]. 2019....
其中,L0范数是用来测量像素改变的数量,L2范数是标准的Euclidean范数。L无穷是用来测量最大像素改变的绝对值。如果所有扰动在这三个矩阵距离下很小,图像也会有相似的表现。作者通过测量L2范数来测量最近的对抗样本距离。在对抗样本的性质中,作者会用到可转移性。