Certified Adversarial Robustness via Randomized Smoothing

【Certified Adversarial Robustness via Randomized Smoothing】的更多相关文章

Certified Adversarial Robustness via Randomized Smoothing

目录概主要内容定理1 代码 Cohen J., Rosenfeld E., Kolter J. Certified Adversarial Robustness via Randomized Smoothing. International Conference on Machine Learning (ICML), 2019. @article{cohen2019certified, title={Certified Adversarial Robustness via Randomiz…

Improving Adversarial Robustness via Channel-Wise Activation Suppressing

目录概主要内容代码 Bai Y., Zeng Y., Jiang Y., Xia S., Ma X., Wang Y. Improving adversarial robustness via channel-wise activation suppressing. In International Conference on Learning Representations (ICLR), 2021. Yan H., Zhang J., Niu G., Feng J., Tan V.,…

Improving Adversarial Robustness Using Proxy Distributions

目录概主要内容 proxy distribution 如何利用构造的数据 Sehwag V., Mahloujifar S., Handina T., Dai S., Xiang C., Chiang M. and Mittal P. Improving adversarial robustness using proxy Distributions. arXiv preprint arXiv: 2104.09425, 2021. 概本文利用GAN生成数据, 并利用这些数据进行对抗训练,…

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

目录概主要内容 Auto-PGD Momentum Step Size 损失函数 AutoAttack Croce F. & Hein M. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International Conference on Machine Learning (ICML), 2020. 概作者改进了PGD攻击方法, 并…

Second Order Optimization for Adversarial Robustness and Interpretability

目录概主要内容 (4)式的求解超参数 Tsiligkaridis T., Roberts J. Second Order Optimization for Adversarial Robustness and Interpretability. arXiv preprint axXiv 2009.04923, 2020. 概也算是一种对抗训练吧, 有区别的是构造对抗样本的方式, 以及用的是惩罚项而非仅用对抗样本训练. 主要内容考虑干净样本\(x\)和扰动\(v\), 则我们自然希望 \…

IMPROVING ADVERSARIAL ROBUSTNESS REQUIRES REVISITING MISCLASSIFIED EXAMPLES

目录概主要内容符号 MART Wang Y, Zou D, Yi J, et al. Improving Adversarial Robustness Requires Revisiting Misclassified Examples[C]. international conference on learning representations, 2020. @article{wang2020improving, title={Improving Adversarial Robustn…

Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! arXiv:2003.10399v2 [cs.CV] 23 Jul 2020 ECCV 2020 1 https://github.com/ssharmin/spikingNN-adversarial-attack Abstract 在最近对可信任的神经网络的探索中,我们提出了一个潜在的候选,即脉冲神经网络(SNN)之于对抗攻击的内在鲁棒性.在这项工作中,我们证明对CIFAR数据集上的深度VGG和ResNet结构,在基于梯度的攻击下,…

Adversarial Detection methods

目录 Kernel Density (KD) Local Intrinsic Dimensionality (LID) Gaussian Discriminant Analysis (GDA) Gaussian Mixture Model (GMM) SelectiveNet Combined Abstention Robustness Learning (CARL) Adversarial Training with a Rejection Option Energy-based Out-of…

Glossary Collection

目录直接修饰用间接强调用 (多为副词) 过渡用特别的名词动词词组各种介词句子摘要引言总结正文实验直接修饰用 Word 含义例句近义词 nuanced adj. 微妙的:具有细微差别的; v. 精确细腻地表演:细致入微地描绘 However, a more nuanced understanding of images arguably requires the ability to reason about how the scene depicted in the…

Competing in a data science contest without reading the data

Competing in a data science contest without reading the data Machine learning competitions have become an extremely popular format for solving prediction and classification problems of all sorts. The most famous example is perhaps the Netflix prize.…