A practical approach to e
A practical approach to evaluating the adversarial distance for machine learning classifiers
A practical approach to evaluating the adversarial distance for machine learning classifiers
arXiv:2409.03598v1 Announce Type: new
Abstract: Robustness is critical for machine learning (ML) classifiers to ensure consistent performance in real-world applications where models may encounter corrupted or adversarial inputs. In particular, assessing the robustness of classifiers to adversarial …
