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Semidefinite relaxations for certifying robustness to adversarial examples
[article]
2018
arXiv
pre-print
Despite their impressive performance on diverse tasks, neural networks fail catastrophically in the presence of adversarial inputs---imperceptibly but adversarially perturbed versions of natural inputs. We have witnessed an arms race between defenders who attempt to train robust networks and attackers who try to construct adversarial examples. One promise of ending the arms race is developing certified defenses, ones which are provably robust against all attackers in some family. These
arXiv:1811.01057v1
fatcat:tfthh5b475dshjxmhvqbzrqr7u