Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations

Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task. While current verification methods mainly focus on the l_p-norm threat model of the input instances, robustness verification against semantic adversarial attacks inducing large l_p-norm perturbations, such as color shifting and lighting adjustment, are beyond their capacity... (read more)

PDF Abstract
No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet