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

• URL: http://arxiv.org/abs/2510.24820v1
• Date: Tue, 28 Oct 2025 15:12:15 GMT
• Title: SafeEditor: Unified MLLM for Efficient Post-hoc T2I Safety Editing
• Authors: Ruiyang Zhang, Jiahao Luo, Xiaoru Feng, Qiufan Pang, Yaodong Yang, Juntao Dai,
• Abstract summary: We propose a multi-round safety editing framework that functions as a model-agnostic, plug-and-play module.<n>We introduce a post-hoc safety editing paradigm that mirrors the human cognitive process of identifying and refining unsafe content.<n>We develop SafeEditor, a unified MLLM capable of multi-round safety editing on generated images.
• Score: 13.35302137193851
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SafeRedir: Prompt Embedding Redirection for Robust Unlearning in Image Generation Models [67.84174763413178]
  We introduce SafeRedir, a lightweight inference-time framework for robust unlearning via prompt embedding redirection.<n>We show that SafeRedir achieves effective unlearning capability, high semantic and perceptual preservation, robust image quality, and enhanced resistance to adversarial attacks.
  arXiv  Detail & Related papers  (2026-01-13T15:01:38Z)
• SafeGuider: Robust and Practical Content Safety Control for Text-to-Image Models [74.11062256255387]
  Text-to-image models are highly vulnerable to adversarial prompts, which can bypass safety measures and produce harmful content.<n>We introduce SafeGuider, a two-step framework designed for robust safety control without compromising generation quality.<n>SafeGuider demonstrates exceptional effectiveness in minimizing attack success rates, achieving a maximum rate of only 5.48% across various attack scenarios.
  arXiv  Detail & Related papers  (2025-10-05T10:24:48Z)
• UpSafe$^\circ$C: Upcycling for Controllable Safety in Large Language Models [67.91151588917396]
  Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak attacks.<n>We propose UpSafe$circ$C, a unified framework for enhancing LLM safety through safety-aware upcycling.<n>Our results highlight a new direction for LLM safety: moving from static alignment toward dynamic, modular, and inference-aware control.
  arXiv  Detail & Related papers  (2025-10-02T16:43:33Z)
• PromptSafe: Gated Prompt Tuning for Safe Text-to-Image Generation [30.2092299298228]
  Text-to-image (T2I) models are vulnerable to producing not-safe-for-work (NSFW) content, such as violent or explicit imagery.<n>We propose PromptSafe, a gated prompt tuning framework that combines a lightweight, text-only supervised soft embedding with an inference-time gated control network.<n>We show that PromptSafe achieves a SOTA unsafe generation rate (2.36%) while preserving high benign fidelity.
  arXiv  Detail & Related papers  (2025-08-02T09:09:40Z)
• HoliSafe: Holistic Safety Benchmarking and Modeling for Vision-Language Model [58.12612140992874]
  We introduce a holistic safety dataset and benchmark, textbfHoliSafe, that spans all five safe/unsafe image-text combinations.<n>We also propose a novel modular framework for enhancing VLM safety with a visual guard module (VGM) designed to assess the harmfulness of input images.<n> Experiments show that Safe-VLM with VGM, trained on our HoliSafe, achieves state-of-the-art safety performance across multiple VLM benchmarks.
  arXiv  Detail & Related papers  (2025-06-05T07:26:34Z)
• MLLM-as-a-Judge for Image Safety without Human Labeling [81.24707039432292]
  In the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content.<n>It is crucial to identify such unsafe images based on established safety rules.<n>Existing approaches typically fine-tune MLLMs with human-labeled datasets.
  arXiv  Detail & Related papers  (2024-12-31T00:06:04Z)
• Safety Without Semantic Disruptions: Editing-free Safe Image Generation via Context-preserving Dual Latent Reconstruction [88.18235230849554]
  Training multimodal generative models on large, uncurated datasets can result in users being exposed to harmful, unsafe and controversial or culturally-inappropriate outputs.<n>We leverage safe embeddings and a modified diffusion process with weighted tunable summation in the latent space to generate safer images.<n>We identify trade-offs between safety and censorship, which presents a necessary perspective in the development of ethical AI models.
  arXiv  Detail & Related papers  (2024-11-21T09:47:13Z)
• SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models [5.6874111521946356]
  Safety-aligned language models often exhibit fragile and imbalanced safety mechanisms.<n>We propose SafeInfer, a context-adaptive, decoding-time safety alignment strategy.<n>HarmEval is a novel benchmark for extensive safety evaluations.
  arXiv  Detail & Related papers  (2024-06-18T05:03:23Z)
• Safety Arithmetic: A Framework for Test-time Safety Alignment of Language Models by Steering Parameters and Activations [19.132597762214722]
  Current alignment methods struggle with dynamic user intentions and complex objectives. We propose Safety Arithmetic, a training-free framework enhancing safety across different scenarios. Our experiments show that Safety Arithmetic significantly improves safety measures, reduces over-safety, and maintains model utility.
  arXiv  Detail & Related papers  (2024-06-17T17:48:13Z)
• Towards Comprehensive Post Safety Alignment of Large Language Models via Safety Patching [74.62818936088065]
  textscSafePatching is a novel framework for comprehensive PSA.<n>textscSafePatching achieves a more comprehensive PSA than baseline methods.<n>textscSafePatching demonstrates its superiority in continual PSA scenarios.
  arXiv  Detail & Related papers  (2024-05-22T16:51:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.