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Found 3 papers, 1 papers with code
Degradation Attacks on Certifiably Robust Neural Networks
Certifiably robust neural networks employ provable run-time defenses against adversarial examples by checking if the model is locally robust at the input under evaluation.
Self-Correcting Neural Networks For Safe Classification
These constraints relate requirements on the order of the classes output by a classifier to conditions on its input, and are expressive enough to encode various interesting examples of classifier safety specifications from the literature.
Fast Geometric Projections for Local Robustness Certification
Local robustness ensures that a model classifies all inputs within an $\ell_2$-ball consistently, which precludes various forms of adversarial inputs.
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