Class-conditional anomaly detection and SHAP-based consistency checks provide a new way to score the quality of tabular adversarial samples, and a seven-attack comparison shows transferability-based attacks are the hardest to detect.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency
Class-conditional anomaly detection and SHAP-based consistency checks provide a new way to score the quality of tabular adversarial samples, and a seven-attack comparison shows transferability-based attacks are the hardest to detect.