[论文总结] Adversarial Examples Are Not Bugs, They Are Features 说在前面 NIPS 2019,原文链接:arxiv.org/abs/1905.0217; 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,今天补上; 近期因为ECCV Rebuttal等事宜,更新较慢; 本文作于2022年05年29日。 1. 解决的问题 近年来,深度神...
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...
Adversarial Examples Are Not Bugs, They Are Features[C]. neural information processing systems, 2019: 125-136.@article{ilyas2019adversarial, title={Adversarial Examples Are Not Bugs, They Are Features}, author={Ilyas, Andrew and Santurkar, Shibani and Tsipras, Dimitris and Engstrom, Logan and ...
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...
【自娱自阅】Self-Supervised Contrastive Learning with Adversarial Examples 633 -- 6:18 App 【自娱自阅】Guided Diffus Model for Adversarial Purification 96 1 26:06 App 【自娱自阅】On the steerability of generative adversarial networks 108 -- 23:21 App 【自娱自阅】Towards the first adversarially robu...
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 ...
③步长a:a减少迁移率提高。 5 讨论与总结 文章最后的讨论我贴在这里,大家可以看一看琢磨一下: 下面说出我自己的理解:迁移攻击说白了就是不同网络对数据集边界的曲线相似度。在文章[AdversarialExamplesAre Not Bugs, They Are Features]中验证过,实际上对抗样本是数据集自身的一 ...
Goh, G. A Discussion of ‘adversarial examples are not bugs, they are features’: two examples of useful, non-robust features. Distill 4, e00019.3 (2019). Google Scholar Denzin, N. K. The Research Act: A Theoretical Introduction to Sociological Methods (Routledge, 2017). Heesen, R., ...
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....