Counterexample-Guided Shortcut Suppression without Shortcut Labels

Spurious correlations (SC) are correlated features in datasets that allow for better benchmark predictions, but are not robust to domain change and so might harm generalisation when relied upon. SC can be unintended, such as background bias in object classification and diagnosis markings in medical images, but they have also been used intentionally, such as in dataset protection. Traditional methods for addressing SC often require expert knowledge and task-specific operations, leading to recent research to detect and correct SC without putting shortcut labels in the training objective. In this paper, we follow this line of research, both when SC is unintended and when it is deliberately injected. We propose a method that does not use shortcut labels during training; instead, we create label-conflicting counterexample mixtures so shortcut-following predictions become costly. We study this as a clean-counterexample supervision regime: biased training data and a small clean counterexample pool are available, but shortcut labels are not used in the training objective. In a controlled hidden-watermark setting, the method suppresses watermark-only prediction to chance, validating the intended mechanism. On augmented Waterbirds, where the shortcut is a replaceable background cue, we compare against previous methods, attaining higher worst-group accuracy without domain labels in the objective. Additional CelebA results test a visually entangled attribute shortcut and show weaker, more variable gains. These results suggest that clean counterexamples can provide useful weak supervision for shortcut attenuation, with effectiveness governed by shortcut separability and counterexample quality.