Detectors Learn the Wrong Thing: Shortcut-Resistant Adversarial Training Against Physically Realizable Attacks
InsCAT is proposed, an instance-level contrastive adversarial training framework that prevents detectors from using adversarial texture as an independent decision cue, and consistent gains across separately trained detectors demonstrate applicability across architectures with direct inference.