Automating Evaluation of Diffusion Model Unlearning with (Vision-) Language Model World Knowledge

TL;DR

This paper presents autoeval-dmun, an automated evaluation tool using language models to assess the effectiveness and impact of unlearning methods in diffusion models, revealing semantic and adversarial insights.

Contribution

The paper introduces autoeval-dmun, a novel automated tool that leverages language models to evaluate diffusion model unlearning comprehensively and efficiently.

Findings

Language models impose semantic orderings correlating with unlearning damage.

Language models can circumvent unlearning with synthetic adversarial prompts.

Automated evaluation reveals limitations of current unlearning methods.

Abstract

Machine unlearning (MU) is a promising cost-effective method to cleanse undesired information (generated concepts, biases, or patterns) from foundational diffusion models. While MU is orders of magnitude less costly than retraining a diffusion model without the undesired information, it can be challenging and labor-intensive to prove that the information has been fully removed from the model. Moreover, MU can damage diffusion model performance on surrounding concepts that one would like to retain, making it unclear if the diffusion model is still fit for deployment. We introduce autoeval-dmun, an automated tool which leverages (vision-) language models to thoroughly assess unlearning in diffusion models. Given a target concept, autoeval-dmun extracts structured, relevant world knowledge from the language model to identify nearby concepts which are likely damaged by unlearning and to circumvent unlearning with adversarial prompts. We use our automated tool to evaluate popular diffusion model unlearning methods, revealing that language models (1) impose semantic orderings of nearby concepts which correlate well with unlearning damage and (2) effectively circumvent unlearning with synthetic adversarial prompts.