Towards automated detection of adversarial attacks on tabular data
Piotr Biczyk, Łukasz Wawrowski · Annals of Computer Science and Information Systems · 2023
The paper presents a novel approach to investigating adversarial attacks on machine learning classification models operating on tabular data.The employed method involves using diagnostic parameters calculated on an approximated representation of a model under attack and analyzing differences in these diagnostic parameters over time.The hypothesis researched by the authors is that adversarial attack techniques, even if attempting a low-profile modification of input data, influence those diagnostic attributes in a statistically significant way.Thus, changes in diagnostic attributes can be used for detecting attack events.Three attack approaches on real-world datasets were investigated.The experiments confirm the approach as a promising technique to be further developed for detecting adversarial attacks.