Hacking Neural Networks: A Short Introduction
arXiv:1911.07658
Abstract
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. In this article, we give 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.
References in corpus (8)
- Adam: A Method for Stochastic Optimization
- Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
- Robust Physical-World Attacks on Deep Learning Models
- Microsoft Malware Classification Challenge
- Adversarial Examples: Attacks and Defenses for Deep Learning
- Invisible Mask: Practical Attacks on Face Recognition with Infrared
- Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
- DeepLaser: Practical Fault Attack on Deep Neural Networks