Unsupervised Unlearnable Segmentation via Semantic Structure Disruption

Unlearnable examples (UEs) aim to prevent machine learning models from extracting useful information in protected training data. Extending UEs to segmentation is practically important but challenging, because pixel-level annotations are expensive to obtain and therefore cannot be assumed available when generating protective perturbations. This motivates us to study unsupervised unlearnable segmentation, where UE perturbations must be generated without segmentation annotations. Since dense prediction fundamentally relies on region-level affinity and cross-layer feature correspondence, effective segmentation data protection faces a twofold design challenge: perturbations should induce misleading alternatives to these structural priors, while the induced structure must remain easy to fit so that downstream segmentation models can adopt it during training. To address these challenges, we propose DRIFT (Disrupting Region-level and Interlayer Feature sTructure) an unsupervised method that generates unlearnable examples for segmentation. Specifically, DRIFT constructs pseudo region-level affinity and pseudo cross-layer correspondence from clean-image features. It then optimizes perturbations to steer the extracted features of protected images toward the misleading pseudo-structure rather than the clean one, while an auxiliary structure learner, trained to predict pseudo-region assignments, explicitly encourages the induced structure to remain easy to fit. Extensive experiments across four segmentation datasets and multiple architectures demonstrate that DRIFT provides strong protection, consistently degrading the generalization performance of models trained on protected data and remaining competitive with the supervised UE baseline.