Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models

Recent research has seen significant interest in methods for concept removal and targeted forgetting in diffusion models. In this paper, we conduct a comprehensive white-box analysis to expose significant vulnerabilities in existing diffusion model unlearning methods. We show that the objective functions used for unlearning in the existing methods lead to decoupling of the targeted concepts (meant to be forgotten) for the corresponding prompts. This is concealment and not actual unlearning, which was the original goal. The ineffectiveness of current methods stems primarily from their narrow focus on reducing generation probabilities for specific prompt sets, neglecting the diverse modalities of intermediate guidance employed during the inference process. The paper presents a rigorous theoretical and empirical examination of four commonly used techniques for unlearning in diffusion models. We introduce two new evaluation metrics: Concept Retrieval Score (CRS) and Concept Confidence Score (CCS). These metrics are based on a successful adversarial attack setup that can recover forgotten concepts from unlearned diffusion models. The CRS measures the similarity between the latent representations of the unlearned and fully trained models after unlearning. It reports the extent of retrieval of the forgotten concepts with increasing amount of guidance. The CCS quantifies the confidence of the model in assigning the target concept to the manipulated data. It reports the probability of the unlearned model's generations to be aligned with the original domain knowledge with increasing amount of guidance. Evaluating existing unlearning methods with our proposed stringent metrics for diffusion models reveals significant shortcomings in their ability to truly unlearn concepts. Source Code: https://respailab.github.io/unlearning-or-concealment

A Unified Framework for Continual Learning and Machine Unlearning

Continual learning and machine unlearning are crucial challenges in machine learning, typically addressed separately. Continual learning focuses on adapting to new knowledge while preserving past information, whereas unlearning involves selectively forgetting specific subsets of data. In this paper, we introduce a novel framework that jointly tackles both tasks by leveraging controlled knowledge distillation. Our approach enables efficient learning with minimal forgetting and effective targeted unlearning. By incorporating a fixed memory buffer, the system supports learning new concepts while retaining prior knowledge. The distillation process is carefully managed to ensure a balance between acquiring new information and forgetting specific data as needed. Experimental results on benchmark datasets show that our method matches or exceeds the performance of existing approaches in both continual learning and machine unlearning. This unified framework is the first to address both challenges simultaneously, paving the way for adaptable models capable of dynamic learning and forgetting while maintaining strong overall performance.