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arXiv · 2510.24820

SafeEditor: Unified MLLM for Efficient Post-hoc T2I Safety Editing

Ruiyang Zhang·Jiahao Luo·Xiaoru Feng·Qiufan Pang·Yaodong Yang·Juntao Dai
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Abstract

With the rapid advancement of text-to-image (T2I) models, ensuring their safety has become increasingly critical. Existing safety approaches can be categorized into training-time and inference-time methods. While inference-time methods are widely adopted due to their cost-effectiveness, they often suffer from limitations such as over-refusal and imbalance between safety and utility. To address these challenges, we propose a multi-round safety editing framework that functions as a model-agnostic, plug-and-play module, enabling efficient safety alignment for any text-to-image model. Central to this framework is MR-SafeEdit, a multi-round image-text interleaved dataset specifically constructed for safety editing in text-to-image generation. We introduce a post-hoc safety editing paradigm that mirrors the human cognitive process of identifying and refining unsafe content. To instantiate this paradigm, we develop SafeEditor, a unified MLLM capable of multi-round safety editing on generated images. Experimental results show that SafeEditor surpasses prior safety approaches by reducing over-refusal while achieving a more favorable safety-utility balance.

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Ruiyang Zhang, Jiahao Luo, Xiaoru Feng, Qiufan Pang, Yaodong Yang, Juntao Dai. 2025-10-28. SafeEditor: Unified MLLM for Efficient Post-hoc T2I Safety Editing. https://arxiv.org/abs/2510.24820

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