  Though much work has been done in the domain of improving the adversarial
robustness of facial recognition systems, a surprisingly small percentage of it
has focused on self-supervised approaches. In this work, we present an approach
that combines Ad-versarial Pre-Training with Triplet Loss
AdversarialFine-Tuning. We compare our methods with the pre-trained ResNet50
model that forms the backbone of FaceNet, finetuned on our CelebA dataset.
Through comparing adversarial robustness achieved without adversarial training,
with triplet loss adversarial training, and our contrastive pre-training
combined with triplet loss adversarial fine-tuning, we find that our method
achieves comparable results with far fewer epochs re-quired during fine-tuning.
This seems promising, increasing the training time for fine-tuning should yield
even better results. In addition to this, a modified semi-supervised experiment
was conducted, which demonstrated the improvement of contrastive adversarial
training with the introduction of small amounts of labels.
