用机器遗忘技术提升LeNet模型对FGSM攻击的抗性。
Protecting the Neural Networks against FGSM Attack Using Machine Unlearning
- 通过重训练移除对抗样本，让模型'忘记'攻击数据。
- 实验显示该方法显著增强LeNet对FGSM攻击的鲁棒性。
- 适合关注模型安全与对抗防御的研究者。
机器学习是构建预测模型的强大工具，但易受对抗攻击影响。快速梯度符号法（FGSM）攻击通过向输入数据添加微小扰动，诱使模型误分类。为应对此类攻击，研究人员提出“机器遗忘”方法，即在不包含扰动数据的情况下重新训练模型。本文聚焦于将遗忘技术应用于经典图像分类网络LeNet，评估其在抵御FGSM攻击中的效果。结果表明，该方法能显著提升LeNet模型对FGSM攻击的鲁棒性。
原文摘要 · Abstract (English)
Machine learning is a powerful tool for building predictive models. However, it is vulnerable to adversarial attacks. Fast Gradient Sign Method (FGSM) attacks are a common type of adversarial attack that adds small perturbations to input data to trick a model into misclassifying it. In response to these attacks, researchers have developed methods for "unlearning" these attacks, which involves retraining a model on the original data without the added perturbations. Machine unlearning is a technique that tries to "forget" specific data points from the training dataset, to improve the robustness of a machine learning model against adversarial attacks like FGSM. In this paper, we focus on applying unlearning techniques to the LeNet neural network, a popular architecture for image classification. We evaluate the efficacy of unlearning FGSM attacks on the LeNet network and find that it can significantly improve its robustness against these types of attacks.
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