IMPROVING ADVERSARIAL ROBUSTNESS REQUIRES REVISITING MISCLASSIFIED EXAMPLES

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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…

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 \…

Certified Adversarial Robustness via Randomized Smoothing

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

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Adversarial Detection methods

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（转）Is attacking machine learning easier than defending it?

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