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- [1412. 6572] Explaining and Harnessing Adversarial Examples - arXiv. org
Abstract: Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence Early attempts at
- [论文笔记]Explaining Harnessing Adversarial Examples - 知乎
这篇文章主要提出与之前论文不同的 线性假设 来解释对抗样本的存在性。 同时，论文提出了一种简单的对抗样本生成方法- FGSM，并且再利用该攻击方法产生的对抗样本进行 对抗训练。 总得来说，这篇文章主要说明的对抗样本的三个方面：1 存在性 、2 攻击方法 、3 防御方法。 原文引自：Goodfellow, Ian J , Jonathon Shlens, and Christian Szegedy "Explaining and harnessing adversarial examples " arXiv preprint arXiv:1412 6572 (2014)
- (PDF) Explaining and Harnessing Adversarial Examples - ResearchGate
We study the structure of adversarial examples and explore network topology, pre-processing and training strategies to improve the robustness of DNNs
- ICLR2015 | FGSM | 解释并利用对抗样本 - CSDN博客
本文 “Explaining and Harnessing Adversarial Examples” 探讨了机器学习模型中的对抗样本现象，认为神经网络易受其攻击的主因是线性性质，提出快速生成方法（Fast Gradient Sign Method, FGSM），经实验表明对抗训练可正则化模型，还讨论了模型容量
- Explaining and Harnessing Adversarial Examples - Google Research
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence
- 《Explaining and harnessing adversarial examples》 - 博客园
论文中提到对抗性例子是由于深度神经网络的极端非线性与模型平均不足和纯监督学习问题的正则化不足相共同导致的。 在高维空间中，线性行为很容易引起对抗性的例子。 这使我们能够设计一种快速生成对抗性示例的方法，从而使对抗性训练变得实用，而对抗性训练也可以带来额外的正则化收益。 一般的正则化策略并不能显著降低模型对对抗例子的脆弱性，但像RBF网络这样的非线性模型族转变可以做到这一点。 通过论文，我们可以发现在设计易于训练的模型 (由于其线性)和设计使用非线性效应来抵抗对抗性扰动的模型之间存在一种基本的张力。 从长远来看，通过设计更强大的优化方法来成功地训练更多的非线性模型，可以避免这种情况。 Szegedy演示了神经网络和相关模型的各种有趣特性。 与论文最相关的包括：
- Explaining and Harnessing Adversarial Examples - Papers With Code
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence
- Explaining and Harnessing Adversarial Examples - INSPIRE
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence
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