对抗训练（adversarial training）是增强神经网络鲁棒性的重要方式。 在对抗训练的过程中，样本会被混合一些微小的扰动（改变很小，但是很可能造成误分类），然后使神经网络适应这种改变，从而对对抗样本具有鲁棒性。

2021年1月3日 · 对抗训练（Adversarial Training）最初由 Ian Goodfellow 等人 [1]提出，作为一种防御对抗攻击的方法，其思路非常简单直接，将生成的对抗样本加入到训练集中去，做一个数据增强，让模型在训练的时…

2024年10月19日 · Adversarial training (AT) refers to integrating adversarial examples -- inputs altered with imperceptible perturbations that can significantly impact model predictions -- into the training process. Recent studies have demonstrated the effectiveness of AT in improving the robustness of deep neural networks against diverse adversarial attacks.

对抗性训练（Adversarial Training）是机器学习领域中一种创新的技术，通过在训练过程中刻意引入精心设计的扰动，增强模型对潜在攻击的防御能力。 不仅提高了模型在面对恶意攻击时的鲁棒性，还提升了其在标准数据上的性能。

2021年2月2日 · Adversarial training is one of the most effective approaches defending against adversarial examples for deep learning models. Unlike other defense strategies, adversarial training aims to promote the robustness of models intrinsically. During the last few years, adversarial training has been studied and discussed from various aspects.

2022年8月12日 · Adversarial training is one of the methods used to defend against the threat of adversarial attacks. It is a training schema that utilizes an alternative objective function to provide model generalization for both adversarial data and clean data. In this systematic review, we focus particularly on adversarial training as a method of improving ...

Adversarial training is one of the most effective ap-proaches to defending deep learning models against adversarial examples. Unlike other defense strate-gies, adversarial training aims to enhance the ro-bustness of models intrinsically. During the last few years, adversarial training has been studied and discussed from various aspects.

1 天前 · Specifically, adversarial training is an effective technique to enhance the robustness of deep neural networks. By generating anti-adversarial examples and adversarial examples, the pseudo label of target data can be corrected further by adversarial training and a more optimal performance in both accuracy and robustness is achieved.

5 天之前 · In contrast, adversarial training methods such as adv.PGD and adv.FGSM demonstrate improved adversarial accuracy at 89.7% and 98.8%, respectively. Table 6 Performance comparison of the proposed approach against FGSM + PGD attacks with state-of-the-art methods for (a) MNIST and (b) CIFAR-10.

2017年5月30日 · k-NN: adversarial training is prone to overfitting. Takeway: neural nets can actually become more secure than other models. Adversarially trained neural nets have the best empirical success rate on adversarial examples of any machine learning model.