Side Effects of Erasing Concepts from Diffusion Models

TL;DR

This paper introduces the Side Effect Evaluation (SEE) benchmark to measure the robustness of concept erasure techniques in diffusion models, revealing their vulnerabilities and side effects such as attribute leakage and ease of circumvention.

Contribution

The paper presents a comprehensive benchmark and evaluation pipeline for assessing the robustness of concept erasure techniques in diffusion models, highlighting their limitations and side effects.

Findings

CETs can be bypassed using hierarchical and compositional prompts.

CETs exhibit attribute leakage and attention dispersal.

Side effects include impact on neighboring concepts and target evasion.

Abstract

Concerns about text-to-image (T2I) generative models infringing on privacy, copyright, and safety have led to the development of concept erasure techniques (CETs). The goal of an effective CET is to prohibit the generation of undesired "target" concepts specified by the user, while preserving the ability to synthesize high-quality images of other concepts. In this work, we demonstrate that concept erasure has side effects and CETs can be easily circumvented. For a comprehensive measurement of the robustness of CETs, we present the Side Effect Evaluation (SEE) benchmark that consists of hierarchical and compositional prompts describing objects and their attributes. The dataset and an automated evaluation pipeline quantify side effects of CETs across three aspects: impact on neighboring concepts, evasion of targets, and attribute leakage. Our experiments reveal that CETs can be circumvented by using superclass-subclass hierarchy, semantically similar prompts, and compositional variants of the target. We show that CETs suffer from attribute leakage and a counterintuitive phenomenon of attention concentration or dispersal. We release our benchmark and evaluation tools to aid future work on robust concept erasure.