Immune-inspired approaches to explainable and robust deep learning models

受免疫启发的方法可解释且稳健的深度学习模型

• 批准号: 2602590
• 金额: --
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2602590
• 关键词: Immune; inspired; approaches; explainable; robust

This project aims to explore the use of artificial immune systems (AIS) to facilitate building deep learning models that are robust against malicious attacks. We are interested in how the immune-inspired approaches can be used as a principled approach to designing robust models that can recognise adversarial attacks, just like how antibodies in our immune systems effectively recognise attacks from viruses or bacteria. We will investigate the potential of major immune-inspired algorithms (i.e., immune network approaches, clonal selection, and negative selection algorithm) as applied to train robust deep learning models by leveraging existing adversarial defence methods and proposing novel defence strategies. Furthermore, within the immune-inspired framework for designing models, we will investigate the design of XAI systems which can explain the predictions of not only normal data samples, but also adversarial attacks. This will help better understand the characteristics of the attacks and provide insights when a deep learning model fails to identify an attack. Such insights will be fed back to our immune-inspired framework for further model improvement.

该项目旨在探索人工免疫系统（AIS）的使用，以促进建立强大的恶意攻击的深度学习模型。我们对如何将免疫启发的方法用作设计可以识别对抗性攻击的强大模型的原则方法感兴趣，就像我们免疫系统中的抗体如何有效地识别病毒或细菌的攻击一样。我们将通过利用现有的对抗性防御方法并提出新颖的防御策略来研究主要免疫启发算法（即免疫网络方法，克隆选择和负面选择算法）的潜力。此外，在设计模型的免疫启发框架内，我们将研究XAI系统的设计，这些设计不仅可以解释不仅是正常数据样本的预测，还可以解释对抗性攻击的预测。这将有助于更好地理解攻击的特征，并在深度学习模型无法识别攻击时提供见解。这些见解将被馈回我们的免疫启发的框架，以进一步改进模型。