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)

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