MOREL: Enhancing Adversarial Robustness through Multi-Objective Representation Learning

We propose MOREL, a method to improve deep neural network robustness against adversarial attacks using a multi-objective optimization approach. Our training method uses an embedding space in which cosine similarity loss and multi-positive contrastive loss are applied to align natural and adversarial features from the model encoder and ensure tight clustering.