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

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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…

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,…

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 \…

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 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…

壁虎书7 Ensemble Learning and Random Forests

if you aggregate the predictions of a group of predictors,you will often get better predictions than with the best individual predictor. a group of predictors is called an ensemble:this technique is called Ensemble Learning,and an Ensemble Learning a…

生成对抗网络资源 Adversarial Nets Papers

来源:https://github.com/zhangqianhui/AdversarialNetsPapers AdversarialNetsPapers The classical Papers about adversarial nets The First paper ✅ [Generative Adversarial Nets] [Paper] [Code](the first paper about it) Unclassified ✅ [Deep Generative Image…