We identify a major weakness in existing machine unlearning methods—namely, their tendency to forget only at the output level while retaining internal representations that allow for data recovery. To address this, we introduce a new theoretical framework called “deep forgetting” based on one-point contraction and propose a practical algorithm, One-Point-Contraction (OPC), which shows strong resilience against known attacks and outperforms existing methods on unlearning benchmarks.
