让模型持续进化，自动应对新型对抗攻击。
Sustainable Self-evolution Adversarial Training
- 分阶段持续学习多种对抗样本，实现动态防御。
- 通过重放关键对抗数据，防止模型遗忘旧知识。
- 适合长期运行的高安全需求视觉系统使用。
随着深度神经网络在计算机视觉任务中的广泛应用，针对模型安全性的对抗样本生成策略不断涌现。然而，现有对抗训练防御模型依赖单一或有限类型的攻击进行一次性学习，难以适应攻击方法的动态演变。为此，我们提出一种可持续自进化对抗训练（SSEAT）框架。该框架引入持续对抗防御流程，实现跨多个阶段对多种对抗样本的学习。为解决持续学习中因新攻击导致的模型灾难性遗忘问题，提出对抗数据重放模块，更优地选择多样且关键的重学数据。同时设计一致性正则化策略，促使当前防御模型从过往训练中汲取更多知识，从而保留历史经验并维持对干净样本的分类准确率。大量实验验证了SSEAT的有效性，其防御性能与分类精度均优于现有方法。代码已开源。
原文摘要 · Abstract (English)
With the wide application of deep neural network models in various computer vision tasks, there has been a proliferation of adversarial example generation strategies aimed at deeply exploring model security. However, existing adversarial training defense models, which rely on single or limited types of attacks under a one-time learning process, struggle to adapt to the dynamic and evolving nature of attack methods. Therefore, to achieve defense performance improvements for models in long-term applications, we propose a novel Sustainable Self-Evolution Adversarial Training (SSEAT) framework. Specifically, we introduce a continual adversarial defense pipeline to realize learning from various kinds of adversarial examples across multiple stages. Additionally, to address the issue of model catastrophic forgetting caused by continual learning from ongoing novel attacks, we propose an adversarial data replay module to better select more diverse and key relearning data. Furthermore, we design a consistency regularization strategy to encourage current defense models to learn more from previously trained ones, guiding them to retain more past knowledge and maintain accuracy on clean samples. Extensive experiments have been conducted to verify the efficacy of the proposed SSEAT defense method, which demonstrates superior defense performance and classification accuracy compared to competitors.Code is available at https://github.com/aup520/SSEAT
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