epoch用多少,应该按weight\theta想要迭代多少步来计算,大致地, 只要算法2训练\theta的步数和 原始的PGD-based adv. training (算法1) 训练\theta的步数一样的步数一样,就能获得和算法1相似的adversarial robustness. the scalability(size) of networks 大一些能够提升一点 adversarial robustness, 同时也会花更长的训...
Our "free" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single work...
We presentan algorithm that eliminates the overhead cost of generating adversarial examplesby recycling the gradient information computed when updating model parameters.Our “free” adversarial training algorithm achieves comparable robustness to PGDadversarial training on the CIFAR-10 and CIFAR-100 datasets...
In this paper we study leveraging confidence information induced by adversarial training to reinforce adversarial robustness of a given adversarially trained model. A natural measure of confidence is $\|F({\bf x})\|_\infty$ (i.e. how confident $F$ is about its prediction?). We start by ...
Official TensorFlow Implementation of Adversarial Training for Free! which trains robust models at no extra cost compared to natural training. attack classification robust adversarial robustness adversarial-learning attack-defense adversarial-machine-learning adversarial-example adversarial-examples adversarial-attack...
We introduce adversarial training into self- supervision, to provide general-purpose robust pretrained models for the first time. We find these robust pretrained models can benefit the subsequent fine-tuning in two ways: i) boosting final model robustness; ii) sa...
To prevent potential overadaptation of new defenses to AutoAttack, we also welcome external evaluations based on adaptive attacks, especially where AutoAttack flags a potential overestimation of robustness. For each model, we are interested in the best known robust accuracy and see AutoAttack and adapti...
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, 并糅合了不同种类的攻击方法于一体, 使得AA的估计更为有效可靠. 特别是不需要调参....
Comparison of our data-free adversarial defense (DAD) with recent data-dependent approaches for resnet18 on CIFAR-10. 7. Conclusion We presented for the first time a complete test time de- tection and correction approach for adversarial robustness in absence of training data. We showed the...
In this paper, we propose a new training paradigm called Guided Complement Entropy (GCE) that is capable of achieving"adversarial defense for free,"which involves no additional procedures in the process of improving adversarial robustness. In addition to maximizing model probabilities on the ground-tr...