Immune-inspired approaches to explainable and robust deep learning models

该项目旨在探索使用人工免疫系统(AIS)来促进建立对恶意攻击具有健壮性的深度学习模型。我们感兴趣的是，如何将免疫启发的方法用作一种有原则的方法，以设计能够识别对手攻击的稳健模型，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。我们将研究主要的免疫启发算法(即免疫网络方法、克隆选择和否定选择算法)的潜力，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健壮的深度学习模型。此外，在免疫启发的模型设计框架内，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洞察。这样的见解将反馈到我们受免疫启发的框架中，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.