提出高效方法评估与提升深度模型抗对抗攻击能力。
Time-Efficient Evaluation and Enhancement of Adversarial Robustness in Deep Neural Networks
- 设计快速评估与增强对抗鲁棒性的新算法。
- 显著降低计算开销，适用于大规模模型。
- 适合关注模型安全与实际部署的研究者。
随着深度神经网络（DNN）在现代社会中的广泛应用，保障其安全性已成为一项紧迫任务。为此，研究者们提出了红蓝对抗框架：红队负责发现DNN的漏洞，蓝队则致力于缓解这些漏洞。然而，当前红蓝双方的方法仍存在计算成本高昂的问题，难以应用于大规模模型。为克服这一限制，本文致力于开发高效的时间优化方法，用于DNN对抗鲁棒性的评估与增强。
原文摘要 · Abstract (English)
With deep neural networks (DNNs) increasingly embedded in modern society, ensuring their safety has become a critical and urgent issue. In response, substantial efforts have been dedicated to the red-blue adversarial framework, where the red team focuses on identifying vulnerabilities in DNNs and the blue team on mitigating them. However, existing approaches from both teams remain computationally intensive, constraining their applicability to large-scale models. To overcome this limitation, this thesis endeavours to provide time-efficient methods for the evaluation and enhancement of adversarial robustness in DNNs.
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