Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts (2410.12777v1)

Abstract: With the rapid progress of diffusion-based content generation, significant efforts are being made to unlearn harmful or copyrighted concepts from pretrained diffusion models (DMs) to prevent potential model misuse. However, it is observed that even when DMs are properly unlearned before release, malicious finetuning can compromise this process, causing DMs to relearn the unlearned concepts. This occurs partly because certain benign concepts (e.g., "skin") retained in DMs are related to the unlearned ones (e.g., "nudity"), facilitating their relearning via finetuning. To address this, we propose meta-unlearning on DMs. Intuitively, a meta-unlearned DM should behave like an unlearned DM when used as is; moreover, if the meta-unlearned DM undergoes malicious finetuning on unlearned concepts, the related benign concepts retained within it will be triggered to self-destruct, hindering the relearning of unlearned concepts. Our meta-unlearning framework is compatible with most existing unlearning methods, requiring only the addition of an easy-to-implement meta objective. We validate our approach through empirical experiments on meta-unlearning concepts from Stable Diffusion models (SD-v1-4 and SDXL), supported by extensive ablation studies. Our code is available at https://github.com/sail-sg/Meta-Unlearning.

• The paper presents a meta-unlearning framework that integrates with standard unlearning objectives to stop diffusion models from relearning unlearned harmful content.
• It formulates the challenge as a bi-level optimization problem, using meta-learning strategies to simulate adversarial finetuning and trigger self-destruction of linked benign concepts.
• Empirical evaluations on Stable Diffusion models reveal significantly lower nudity scores and maintained generative quality, demonstrating strong resistance against adversarial attacks.

Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts

The paper addresses a crucial challenge in the domain of diffusion models (DMs)—specifically, the unintended relearning of unlearned concepts via malicious finetuning. This phenomenon can negate the efforts made to unlearn harmful or copyrighted content from pretrained models. The proposed solution, termed meta-unlearning, offers a robust framework compatible with existing unlearning methodologies.

Unlearning Challenges

Despite advancements in machine unlearning algorithms for DMs, there is a persistent vulnerability: unlearned models retain certain benign concepts related to the unlearned content. This linkage fosters the possibility of relearning when models undergo finetuning. For example, while "nudity" might be unlearned, the related concept "skin" remains, enabling potential relearning. Addressing this requires a strategy that not only unlearns specific concepts but also ensures resistance against adversarial finetuning.

Meta-Unlearning Framework

The meta-unlearning framework introduces an additional layer to existing unlearning techniques:

1. Standard Unlearning Objective: This ensures the model forgets specified harmful or proprietary data while maintaining performance on benign data. Methods like ESD, SDD, UCE, and RECE serve as the foundational techniques.
2. Meta Objective: A novel component of the framework, the meta objective works to hinder the model from relearning unlearned concepts. Upon malicious finetuning, it triggers self-destruction of related benign concepts, thereby fortifying the model against adversarial attacks.

The meta-unlearning procedure is constructed as a bi-level optimization problem, akin to methodologies in meta-learning. It meticulously simulates potential finetuning scenarios to prepare the model preemptively. This proactive approach is encapsulated elegantly in Algorithm 1 of the paper, which delineates the framework's integration with conventional unlearning processes.

Empirical Evaluation

Empirical assessments were conducted using Stable Diffusion models (SD-v1-4 and SDXL). Evaluation metrics included FID and CLIP scores for generative quality, alongside specific tests for unlearning efficacy using the nudity score on datasets containing sensitive content. The meta-unlearned models demonstrate resilience, exhibiting significantly lower nudity scores post-finetuning compared to their unlearned counterparts.

Qualitative analyses further corroborate these findings. Visual comparisons clearly illustrate that meta-unlearned models resist relearning even after extensive adversarial exposure. This resilience applies across various domains, including the prevention of generating removed copyrights or artistic styles.

Implications and Future Directions

The meta-unlearning framework bears significant implications for the security and ethical deployment of diffusion models. By preventing the inadvertent relearning of unlearned concepts, it provides a robust safety net against misuse. Future exploration could refine this approach by optimizing the meta objective's integration with more complex unlearning algorithms. Additionally, broader tests across various model architectures and domains would strengthen its applicability.

Moreover, the framework's straightforward integration with existing methods invites potential synergy between different unlearning strategies, perhaps evolving a more unified and efficient approach to machine unlearning. Continuously enhancing the robustness of such models against adversarial strategies remains a critical avenue for research, aiming for ever-greater alignment with ethical AI principles.

In summary, this paper introduces a pivotal advancement in DM safety by effectively bridging gaps in existing unlearning mechanisms through meta-learning-inspired methodologies. The proposed meta-unlearning framework stands as a testament to the ongoing refinement of generative models in pursuit of secure and ethical applications.