Related papers: SafeCtrl: Region-Aware Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress

SafeCtrl: Region-Aware Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress

URL: http://arxiv.org/abs/2604.03941v1
Date: Sun, 05 Apr 2026 03:06:17 GMT
Title: SafeCtrl: Region-Aware Safety Control for Text-to-Image Diffusion via Detect-Then-Suppress
Abstract summary: We propose SafeCtrl, a Region-Aware safety control framework operating on a Detect-Then-Suppress paradigm.<n>Unlike global safety interventions, SafeCtrl first employs an attention-guided Detect module to precisely localize specific risk regions.<n>We show that SafeCtrl achieves a superior trade-off between safety and fidelity compared to state-of-the-art methods.
Score: 13.834468340496414
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: The widespread deployment of text-to-image diffusion models is significantly challenged by the generation of visually harmful content, such as sexually explicit content, violence, and horror imagery. Common safety interventions, ranging from input filtering to model concept erasure, often suffer from two critical limitations: (1) a severe trade-off between safety and context preservation, where removing unsafe concepts degrades the fidelity of the safe content, and (2) vulnerability to adversarial attacks, where safety mechanisms are easily bypassed. To address these challenges, we propose SafeCtrl, a Region-Aware safety control framework operating on a Detect-Then-Suppress paradigm. Unlike global safety interventions, SafeCtrl first employs an attention-guided Detect module to precisely localize specific risk regions. Subsequently, a localized Suppress module, optimized via image-level Direct Preference Optimization (DPO), neutralizes harmful semantics only within the detected areas, effectively transforming unsafe objects into safe alternatives while leaving the surrounding context intact. Extensive experiments across multiple risk categories demonstrate that SafeCtrl achieves a superior trade-off between safety and fidelity compared to state-of-the-art methods. Crucially, our approach exhibits improved resilience against adversarial prompt attacks, offering a precise and robust solution for responsible generation.

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