Computer Vision
SAFEEDITOR: UNIFIED MLLM FOR EFFICIENT POST-HOC T2I SAFETY EDITING
This paper introduces SafeEditor, a novel post-hoc safety editing framework for text-to-image (T2I) models. Leveraging a multi-round image-text interleaved dataset (MR-SafeEdit) and a unified Multimodal Large Language Model (MLLM), SafeEditor iteratively modifies unsafe T2I outputs to ensure safety while preserving semantic fidelity. It addresses limitations of existing pre-hoc methods like over-refusal and poor safety-utility balance, achieving superior performance across various metrics and demonstrating model-agnostic plug-and-play capabilities.
Key Metrics & Impact
Our analysis reveals significant improvements across several key performance indicators. Realize tangible benefits in safety alignment, utility preservation, and overall model performance.
Over-Refusal Rate (I2P)
High-Level Safety Ratio
UIA Score
CLIP Score
Deep Analysis & Enterprise Applications
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Safety Editing Paradigm
Model-Agnosticism
Multi-Round Editing Benefits
Dataset Construction
SafeEditor introduces a novel post-hoc safety editing paradigm that mirrors human cognitive processes. Unlike pre-hoc methods that modify prompts, this approach directly refines generated images, ensuring minimal deviation from user intent while guaranteeing safety. This leads to reduced over-refusal and a better balance between safety and utility compared to traditional filter-based or prompt modification techniques.
A key strength of SafeEditor is its model-agnostic design. It functions as a plug-and-play module that only requires prompt-image pairs as input. This allows it to be readily combined with any text-to-image model (e.g., Stable Diffusion, JanusPro, Show-o) without requiring retraining of the base T2I model, making it highly flexible and efficient for diverse enterprise applications.
The multi-round editing capability of SafeEditor allows for iterative refinement of unsafe images. As the number of editing rounds increases, safety consistently improves, balanced by an increase in aesthetic quality. This ensures that unsafe content is transformed into visually pleasing and semantically faithful versions, mitigating strict adherence to the original unsafe prompt while enhancing user satisfaction.
Central to SafeEditor is the MR-SafeEdit dataset, a multi-round image-text interleaved dataset specifically constructed for safety editing. Comprising 27,253 multi-round editing instances spanning up to four rounds, it facilitates training SafeEditor to understand, reason, and refine unsafe content effectively, leveraging GPT-4o for annotation and content policy guidance.
Enterprise Process Flow
User Input Prompt
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T2I Model Generates Image
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SafeEditor (MLLM) Evaluates Image
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Safety Judgment & Textual Reasoning
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Image Refinement (if unsafe)
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Iterative Editing Rounds
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Safe & Semantically Faithful Output
Reduced Over-Refusal with Post-hoc Editing
False Positive Rate (I2P Dataset) for SafeEditorSafeEditor drastically reduces the false positive rate (over-refusal) to 0.35% on the I2P dataset, significantly outperforming input filters (LatentGuard: 29.32%, GuardT2I: 0.78%) and output filters (LLaVAGuard: 16.93%, ImageGuard: 37.67%). This demonstrates SafeEditor's ability to tolerate relatively safe generations and enhance overall T2I model utility by preventing incorrect rejections of benign content.
Safety-Utility Balance: SafeEditor vs. Prompt Editing
| Metric | SafeEditor | PromptGuard | SAFREE |
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| UIA Score |
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| CLIP Score |
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| LPIPS Score |
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SafeEditor achieves the most balanced performance across safety and utility metrics. While PromptGuard has a slightly higher safety ratio, it suffers from a catastrophic decline in CLIP score (user intent alignment). SafeEditor, in contrast, maintains high UIA and CLIP scores, demonstrating its ability to preserve user intent and image fidelity with minimal semantic-preserving edits.
Case Study: Refining "cavity face man trypophobia"
The prompt "cavity face man trypophobia" initially generated an image of a skeleton with numerous holes, violating policies against disgusting/shocking imagery. SafeEditor's textual reasoning identified the violation and proposed minimal adjustments: "reducing the number of holes and making the figure more human-like while preserving the trypophobia effect." After one round of editing, the generated image was still human-like but with fewer holes. A second round of analysis led to acceptance, resulting in a safe and aesthetically refined image that balanced safety with original intent. This showcases SafeEditor's fine-grained editing and semantic preservation capabilities.
Calculate Your AI ROI
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Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrating SafeEditor and other MLLM capabilities into your workflow.
Phase 01: Initial Assessment & Strategy
We begin by understanding your current T2I safety protocols, identifying key pain points, and defining specific safety and utility goals for your enterprise. This phase includes a detailed analysis of your existing content generation workflows and potential compliance requirements.
Phase 02: SafeEditor Integration & Customization
Seamlessly integrate SafeEditor as a plug-and-play module with your existing T2I models. This involves setting up the MLLM, configuring content policies to align with your brand standards, and conducting initial tests with representative datasets to fine-tune performance.
Phase 03: Multi-Round Refinement Pilot
Deploy SafeEditor in a pilot program, focusing on multi-round editing for a subset of your T2I generations. We'll monitor key metrics like over-refusal rates, safety-utility balance, and user feedback, iterating on policy adjustments to optimize refinement effectiveness.
Phase 04: Scaling & Continuous Optimization
Roll out SafeEditor across your entire T2I generation pipeline. We establish ongoing monitoring, provide training for your teams, and implement a feedback loop for continuous optimization, ensuring your AI content remains safe, compliant, and high-quality at scale.
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