有很多种方法能够生成对抗样本(adversarial samples), 但是真实世界中是否存在这样的对抗样本呢? 主要内容 least likely class adv. 假设 为图像(各元素取值为 ), 为其标签, 为一模型, 其输出是一个概率向量, 定义 故本文的生成adversarial samples的方法是最小化 则 其中 即使得 落入 内且, . 实验1 l.l.c...
Goodfellow, Samy Bengio, ADVERSARIAL EXAMPLES IN THE PHYSICAL WORLD概有很多种方法能够生成对抗样本(adversarial samples), 但是真实世界中是否存在这样的对抗样本呢?主要内容least likely class adv.假设XX为图像(各元素取值为[0,255][0,255]), ytrueytrue为其标签, f(X)f(X)为一模型, 其输出是一个概率...
1.引言 这篇文章由Goodfellow等人发表在ICLR2017会议上,是对抗样本领域的经典论文。这篇文章与以往不同的,主要是通过摄像头等传感器输入对抗样本到Inceptionv3,相当于在物理世界的实际攻击。同时论文提出BIM和ILCM对抗样本生成方法,并且与之前提出的fgsm在imagenet 验证集上进行效果比较。总的来说,这篇文章提出新的攻击...
Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples. Besides the attacks in the digital world, the practical implications of adversarial examples in the physical world present significant challenges and safety concerns. However, current research on physical adversarial ...
This paper comprehensively investigates the attack work of adversarial examples in the physical world. Firstly, the related concepts of adversarial examples and typical generation algorithms are introduced, with the purpose of discussing the challenges of adversarial attacks in the physical world. Then, ...
In addition, the relevant feasible defense strategies are summarized. Finally, relying on the reviewed work, we propose potential research directions for the attack and defense of adversarial examples in the physical world.doi:10.1007/s13042-020-01242-zRen, Huali...
Adversarial examples in the physical world, Goodfellow et al, 2017 Explaining and harnessing adversarial examples, Goodfellow et al, 2015 Distillation as a defense to adversarial perturbations against deep neural networks, Papernot et al., 2016 ...
[30] Jan Hendrik Metzen. Mummadi Chaithanya Kumar. Thomas Brox. and Volker Fischer. Universal adversarial perturbations against semantic image segmentation. In ICCV, 2017. [31] Alexey Kurakin. Ian J. Goodfellow. and Samy Bengio.Adversarial examples in the physical world. CoRR. vol. abs/1607.02533...
This is not always the case for systems operating in the physical world, for example those which are using signals from cameras and other sensors as an input. This paper shows that even in such physical world scenarios, machine learning systems are vulnerable to adversarial examples. We ...
2019.11.15 note (1) EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES FGSM (Fast Gradient Sign Method) Adversarial examples in the physical world Another version: Towards Deep Learning Models Resistant 关于对抗训练的记录 最近看了一些关于对抗训练的论文,作一些笔记以备以后可以查看回顾。 现实中的时间序列或...