Robustness verification of ReLU networks via quadratic programming

Aleksei Kuvshinov, Stephan Günnemann

Research output: Contribution to journalArticlepeer-review


Neural networks are known to be sensitive to adversarial perturbations. To investigate this undesired behavior we consider the problem of computing the distance to the decision boundary (DtDB) from a given sample for a deep neural net classifier. In this work we present a procedure where we solve a convex quadratic programming (QP) task to obtain a lower bound on the DtDB. This bound is used as a robustness certificate of the classifier around a given sample. We show that our approach provides better or competitive results in comparison with a wide range of existing techniques.

Original languageEnglish
Pages (from-to)2407-2433
Number of pages27
JournalMachine Learning
Issue number7
StatePublished - Jul 2022


  • Machine learning
  • Minimal adversarial perturbation
  • Neural networks
  • Quadratic programming
  • Robustness verification


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