While deep neural networks have shown many amazing results in various applications, their large memory footprint hinders their usage on resource-constrained IoT devices.
Recently we saw many efforts on deep model compression for embedded devices. However, these models can be easily fooled by simple tricks and attacks, e.g., adding a small perturbation to a panda image fools the network to classify it as a gibbon.
The goal of this thesis is to secure compressed neural networks, so that they are robust against different types of machine learning hacks, e.g., adversarial attacks.
While working on the topic, you will receive different compressed networks. Your job is to leverage published and open-sourced algorithms to attack these compressed models and bring your own ideas on how to improve model robustness.