This paper proposes a regularization method called GReAT, Graph Regularized
Adversarial Training, to improve deep learning models' classification
performance. Adversarial examples are a well-known challenge in machine
learning, where small, purposeful perturbations to input data can mislead
models. Adversarial training, a powerful and one of the most effective defense
strategies, involves training models with both regular and adversarial
examples. However, it often neglects the underlying structure of the data. In
response, we propose GReAT, a method that leverages data graph structure to
enhance model robustness. GReAT deploys the graph structure of the data into
the adversarial training process, resulting in more robust models that better
generalize its testing performance and defend against adversarial attacks.
Through extensive evaluation on benchmark datasets, we demonstrate GReAT's
effectiveness compared to state-of-the-art classification methods, highlighting
its potential in improving deep learning models' classification performance.

本文提出一种名为 GReAT 的正则化方法，以提高深度学习模型的分类性能，通过将数据图结构引入对抗性训练过程中，增强模型的鲁棒性，并在对抗攻击中提供更好的测试性能和防御能力。