  State-of-the-art deep learning models for tabular data have recently achieved
acceptable performance to be deployed in industrial settings. However, the
robustness of these models remains scarcely explored. Contrary to computer
vision, there are no effective attacks to properly evaluate the adversarial
robustness of deep tabular models due to intrinsic properties of tabular data,
such as categorical features, immutability, and feature relationship
constraints. To fill this gap, we first propose CAPGD, a gradient attack that
overcomes the failures of existing gradient attacks with adaptive mechanisms.
This new attack does not require parameter tuning and further degrades the
accuracy, up to 81% points compared to the previous gradient attacks. Second,
we design CAA, an efficient evasion attack that combines our CAPGD attack and
MOEVA, the best search-based attack. We demonstrate the effectiveness of our
attacks on five architectures and four critical use cases. Our empirical study
demonstrates that CAA outperforms all existing attacks in 17 over the 20
settings, and leads to a drop in the accuracy by up to 96.1% points and 21.9%
points compared to CAPGD and MOEVA respectively while being up to five times
faster than MOEVA. Given the effectiveness and efficiency of our new attacks,
we argue that they should become the minimal test for any new defense or robust
architectures in tabular machine learning.
