A Formalization of Robustness for Deep Neural Networks
arXiv:1903.10033
Abstract
Deep neural networks have been shown to lack robustness to small input perturbations. The process of generating the perturbations that expose the lack of robustness of neural networks is known as adversarial input generation. This process depends on the goals and capabilities of the adversary, In this paper, we propose a unifying formalization of the adversarial input generation process from a formal methods perspective. We provide a definition of robustness that is general enough to capture different formulations. The expressiveness of our formalization is shown by modeling and comparing a variety of adversarial attack techniques.
References in corpus (10)
- Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
- ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
- Towards Deep Learning Models Resistant to Adversarial Attacks
- Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
- DeepXplore: Automated Whitebox Testing of Deep Learning Systems
- Poisoning Attacks against Support Vector Machines
- Provable defenses against adversarial examples via the convex outer adversarial polytope
- Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
- Towards Verified Artificial Intelligence
- Systematic Testing of Convolutional Neural Networks for Autonomous Driving