I am a high school student, and I’ve been working on a very niche field featuring the intersection of 3D deep learning and adversarial robustness.

My recent paper is on generating adversarial point sets against neural networks like PointNet. In total, there are four novel attacks in two categories: distributional and shape attacks. Distributional attacks barely change the shape of a point set because we use the shape-aware Hausdorff distance instead of a p-norm. Shape attacks are focused on changing the shape (that results in changes to the point clouds), which is realistic and robust against point removal defenses to restore an adversarial point cloud that some other groups and I have previously proposed. Note that these shape attacks do not need any extra info other than the raw 3D points.

You can read about point set adversarial attacks in my blog post: https://blog.liudaniel.com/birth-of-a-new-sub-sub-field

Or read my latest paper: https://arxiv.org/abs/1908.06062