Unlike 2D data, 3D data is complex with more parameters and features. Collecting 3D data and transforming it from one representation to another is a tedious process. Thus 3D deep learning is more time consuming and error-prone than 2D Computer Vision. Though there are nicely-performing models, datasets, metrics, graphics tools, and visualization tools published in recent years, integrating different approaches is quite a non-trivial job for researchers and practitioners. In this scenario, NVIDIA introduced a PyTorch-based library named Kaolin and has recently released its latest optimized version.

A large chunk of research on the security issues of neural networks is focused on adversarial attacks. However, there exists a vast sea of simpler attacks one can perform both against and with neural networks. This paper gives a quick introduction on how deep learning in security works and explore the basic methods of exploitation, but also look at the offensive capabilities deep learning enabled tools provide. All presented attacks, such as backdooring, GPU-based buffer overflows or automated bug hunting, are accompanied by short open-source exercises for anyone to try out. The TensorFlow code for this paper can be found HERE.

A large chunk of research on the security issues of neural networks is focused on adversarial attacks. However, there exists a vast sea of simpler attacks one can perform both against and with neural networks. This paper gives a quick introduction on how deep learning in security works and explore the basic methods of exploitation, but also look at the offensive capabilities deep learning enabled tools provide. All presented attacks, such as backdooring, GPU-based buffer overflows or automated bug hunting, are accompanied by short open-source exercises for anyone to try out. The TensorFlow code for this paper can be found HERE.