论文标题
学会从错误中学习：对对抗噪声的强大优化
Learning to Learn from Mistakes: Robust Optimization for Adversarial Noise
论文作者
论文摘要
对对抗性噪声阻碍机器学习算法的敏感性。尽管已经提出了许多对抗性防御，但对对抗性噪声的鲁棒性仍然是一个开放的问题。最引人注目的防御，对抗性训练需要大幅度增加处理时间，并且已证明它在训练数据上过高。在本文中，我们的目标是通过在低数据制度中培训强大的模型来克服这些局限性，并在不同模型之间传递对抗知识。我们训练一个元射击器，该次数学会使用对抗性示例来稳健地优化模型，并能够将所学知识传输到新模型，而无需生成新的对抗性示例。实验结果表明，在不同的体系结构和数据集中，荟萃激进器是一致的，这表明可以自动修补对抗性漏洞。
Sensitivity to adversarial noise hinders deployment of machine learning algorithms in security-critical applications. Although many adversarial defenses have been proposed, robustness to adversarial noise remains an open problem. The most compelling defense, adversarial training, requires a substantial increase in processing time and it has been shown to overfit on the training data. In this paper, we aim to overcome these limitations by training robust models in low data regimes and transfer adversarial knowledge between different models. We train a meta-optimizer which learns to robustly optimize a model using adversarial examples and is able to transfer the knowledge learned to new models, without the need to generate new adversarial examples. Experimental results show the meta-optimizer is consistent across different architectures and data sets, suggesting it is possible to automatically patch adversarial vulnerabilities.
