Erased or Dormant? Rethinking Concept Erasure Through Reversibility

TL;DR

This paper critically evaluates the effectiveness of current concept erasure techniques in diffusion models, revealing that they often only superficially suppress concepts without fully erasing their generative capacity, which can reemerge after minimal adaptation.

Contribution

The study introduces an instance-level evaluation method to test the true erasure of concepts and demonstrates that existing techniques often fail to achieve irreversible removal.

Findings

Erased concepts can reemerge after minimal fine-tuning

Current methods often only superficially suppress targeted concepts

Deeper, representation-level interventions are needed for true erasure

Abstract

To what extent does concept erasure eliminate generative capacity in diffusion models? While prior evaluations have primarily focused on measuring concept suppression under specific textual prompts, we explore a complementary and fundamental question: do current concept erasure techniques genuinely remove the ability to generate targeted concepts, or do they merely achieve superficial, prompt-specific suppression? We systematically evaluate the robustness and reversibility of two representative concept erasure methods, Unified Concept Editing and Erased Stable Diffusion, by probing their ability to eliminate targeted generative behaviors in text-to-image models. These methods attempt to suppress undesired semantic concepts by modifying internal model parameters, either through targeted attention edits or model-level fine-tuning strategies. To rigorously assess whether these techniques truly erase generative capacity, we propose an instance-level evaluation strategy that employs lightweight fine-tuning to explicitly test the reactivation potential of erased concepts. Through quantitative metrics and qualitative analyses, we show that erased concepts often reemerge with substantial visual fidelity after minimal adaptation, indicating that current methods suppress latent generative representations without fully eliminating them. Our findings reveal critical limitations in existing concept erasure approaches and highlight the need for deeper, representation-level interventions and more rigorous evaluation standards to ensure genuine, irreversible removal of concepts from generative models.