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 p-norm threat model of the input instances, robustness verification against semantic adversarial attacks inducing large p-norm perturba...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021
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| Online Access: | https://hdl.handle.net/1721.1/130001 |