Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers — mentioned by c0deb0t

Thu, 03 Oct 2019 22:46:09 GMT

If you have any questions, I (the first author of the paper) will be more than happy to answer them. This is my second work in a series on adversarial attacks/defenses in 3D space (first paper [1]). A high-level overview of the research: Basically neural networks are weak against adversarial attacks that change the input by a little bit to cause the prediction to be wrong. We look at these adversarial attacks in 3D space, specifically on 3D point clouds (think LiDAR and RGB-D data). In the paper, four attacks in two different categories (distributional and shape attacks) are proposed. The…

Paper and discussion history. A mention is not an endorsement.

Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers · HackerPapers discussions

Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers — mentioned by c0deb0t