Title: OPC: One-Point-Contraction Unlearning Toward Deep Feature Forgetting
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Unlearning, Feature forgetting

Abstract: Machine unlearning seeks to remove the influence of specific data or classes from trained models to meet privacy or legal requirements. However, existing methods often achieve only shallow forgetting: while outputs change, internal representations still retain enough information to reconstruct the forgotten data or behavior. We demonstrate this vulnerability via feature and data reconstruction attacks, showing that most unlearned features remain informative enough to recover both model performance and raw inputs from the forget set. To address this issue, we propose OPC (One-Point Contraction), a simple yet effective unlearning method that contracts the output representations of forget data toward the origin. By limiting representational capacity to a single point, OPC selectively erases feature-level information associated with the forget set. Empirical evaluations on image classification benchmarks show that OPC achieves strong unlearning efficacy and superior robustness against recovery and reconstruction attacks. We further extend OPC to generative diffusion models, validating its effectiveness in the context of conditional image generation. Applied to Stable Diffusion, OPC enables fine-grained removal of concept-level information, achieving state-of-the-art performance in generative unlearning. These results demonstrate OPC’s broad applicability and its potential for precise, task-aware control of forgetting across both discriminative and generative domains.