Beyond Suppression: Measuring Concept Erasure Durability in Diffusion Models

This paper first investigates a vulnerability that quantization can reverse concept erasure in diffusion models. To this end, we first quantize diffusion models edited by different concept-erasure methods, and the results demonstrate that the target concept remains suppressed after quantization. However, the quantized erased model exhibits a novel vulnerability: restoring just 5% of the edited weights to their original values can markedly increase the generation rate of the erased concept. In this paper, we refer to the ability to keep suppression under such restoration as erasure durability and evaluate it by tracking target generation across restoration budgets. This yields a recovery curve showing how target generation changes as the restoration budget increases. We summarize this curve with the half-recovery budget, defined as the minimum restoration needed to raise the target-generation rate to the midpoint between its erased-model baseline and fully restored level. Based on this, we construct DuraBench, a durability-based benchmark that distinguishes erasure methods with similar initial suppression but different resistance to weight restoration. We further propose SPARC, a post-training method that helps erased models maintain target suppression when some edited weights are restored.