Direct Unlearning Optimization for Robust and Safe Text-to-Image Models

TL;DR

This paper introduces DUO, a new method for effectively removing unsafe content from text-to-image models while maintaining their ability to generate safe images, enhancing model safety and reliability.

Contribution

The paper proposes Direct Unlearning Optimization (DUO), a novel framework that robustly removes unsafe content from T2I models without degrading performance on unrelated topics.

Findings

DUO effectively defends against adversarial attacks.

Maintains high image quality on safe content.

Outperforms existing unlearning methods in safety and performance.

Abstract

Recent advancements in text-to-image (T2I) models have unlocked a wide range of applications but also present significant risks, particularly in their potential to generate unsafe content. To mitigate this issue, researchers have developed unlearning techniques to remove the model's ability to generate potentially harmful content. However, these methods are easily bypassed by adversarial attacks, making them unreliable for ensuring the safety of generated images. In this paper, we propose Direct Unlearning Optimization (DUO), a novel framework for removing Not Safe For Work (NSFW) content from T2I models while preserving their performance on unrelated topics. DUO employs a preference optimization approach using curated paired image data, ensuring that the model learns to remove unsafe visual concepts while retaining unrelated features. Furthermore, we introduce an output-preserving regularization term to maintain the model's generative capabilities on safe content. Extensive experiments demonstrate that DUO can robustly defend against various state-of-the-art red teaming methods without significant performance degradation on unrelated topics, as measured by FID and CLIP scores. Our work contributes to the development of safer and more reliable T2I models, paving the way for their responsible deployment in both closed-source and open-source scenarios.