State-of-the-art models are susceptible to adversarial attacks. These attacks can cause catastrophic misclassification when robustness is required. With the increasing popularity of the retrieval augmentation paradigm in deep learning, we adopt it as a fully differential framework for adversarial robustness. We evaluate our method on three visual classification datasets, including ImageNet and attack our model with two white box attacks and a black box attack under various L₂ and L_∞ norms. The results indicate that a robust classifier emerges if the model fully relies on retrieved examples. We find that we can already obtain a PGD robust ImageNet classifier with 80.1% clean and 64.7% adversarial accuracy, using only one or two examples per class from the training data in the memory set. Contrary to other adversarial defense mechanisms, our method works directly on top of pre-trained models and remains robust when other defenses start to degrade for PGD attacks increasing in strength.

With the world in grasp of the COVID-19 pandemic, models predicting the spread of the virus can give indications to what extent a country is controlling the pandemic. Policymakers can decide to install so-called mitigation strategies to limit the spread of the virus. To aid the decision-making process, this report describes how a web application was created that is capable of visualising predictions on the future course of the virus spread in the Netherlands. Furthermore, the application allows for changing the spread rate of the virus to simulate both mitigation and exit strategies. Research has been conducted on how we can combine predictions and simulations of mitigation strategies in a single visual solution, in order to aid policymakers. Existing products were analysed in order to get a better understanding of the users’ wishes. Design goals were established which have been taken into account when designing and building the software. Furthermore, suitable languages and frameworks for the implementation were chosen. We have created a tool which both implements a prediction algorithm and visualises the outcomes of this algorithm in a web application. First, a visual design of the product was created after which an accompanying software architecture was established. This design and architecture were then implemented and tested accordingly. Most of the conducted tests were unit tests, but also user tests were performed. During the implementation phase, potential ethical consequences were considered and handled accordingly