One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing Framework
Journal:
arXiv
Published Date:
May 16, 2025
Abstract
Concept erasing has recently emerged as an effective paradigm to prevent
text-to-image diffusion models from generating visually undesirable or even
harmful content. However, current removal methods heavily rely on manually
crafted text prompts, making it challenging to achieve a high erasure
(efficacy) while minimizing the impact on other benign concepts (usability). In
this paper, we attribute the limitations to the inherent gap between the text
and image modalities, which makes it hard to transfer the intricately entangled
concept knowledge from text prompts to the image generation process. To address
this, we propose a novel solution by directly integrating visual supervision
into the erasure process, introducing the first text-image Collaborative
Concept Erasing (Co-Erasing) framework. Specifically, Co-Erasing describes the
concept jointly by text prompts and the corresponding undesirable images
induced by the prompts, and then reduces the generating probability of the
target concept through negative guidance. This approach effectively bypasses
the knowledge gap between text and image, significantly enhancing erasure
efficacy. Additionally, we design a text-guided image concept refinement
strategy that directs the model to focus on visual features most relevant to
the specified text concept, minimizing disruption to other benign concepts.
Finally, comprehensive experiments suggest that Co-Erasing outperforms
state-of-the-art erasure approaches significantly with a better trade-off
between efficacy and usability. Codes are available at
https://github.com/Ferry-Li/Co-Erasing.
