Immune-inspired approaches to explainable and robust deep learning models

该项目旨在探索人工免疫系统（AIS）的使用，以促进构建对恶意攻击具有鲁棒性的深度学习模型。我们感兴趣的是如何将免疫启发方法用作设计能够识别对抗性攻击的鲁棒模型的原则方法，就像我们免疫系统中的抗体如何有效识别来自病毒或细菌的攻击一样。我们将研究主要免疫启发算法的潜力（即，免疫网络方法、克隆选择和阴性选择算法），通过利用现有的对抗性防御方法和提出新的防御策略来训练强大的深度学习模型。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，该系统不仅可以解释正常数据样本的预测，还可以解释对抗性攻击的预测。这将有助于更好地了解攻击的特征，并在深度学习模型无法识别攻击时提供见解。这些见解将反馈到我们的免疫启发框架中，以进一步改进模型。

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.