ReVision : A Post-Hoc, Vision-Based Technique for Replacing Unacceptable Concepts in Image Generation Pipeline

TL;DR

ReVision is a training-free, post-hoc safety framework for image generation that detects and edits unsafe concepts in images, improving safety and scene fidelity without retraining the generator.

Contribution

ReVision introduces a novel, prompt-based, post-hoc safety method with a VLM-assisted spatial gating mechanism for precise unsafe concept editing in images.

Findings

Improves CLIP alignment toward safe prompts by +0.121 on average.

Significantly enhances multi-concept background fidelity (LPIPS 0.166 to 0.058).

Reduces recognizability of policy-violating content from 95.99% to 10.16%.

Abstract

Image-generative models are widely deployed across industries. Recent studies show that they can be exploited to produce policy-violating content. Existing mitigation strategies primarily operate at the pre- or mid-generation stages through techniques such as prompt filtering and safety-aware training/fine-tuning. Prior work shows that these approaches can be bypassed and often degrade generative quality. In this work, we propose ReVision, a training-free, prompt-based, post-hoc safety framework for image-generation pipeline. ReVision acts as a last-line defense by analyzing generated images and selectively editing unsafe concepts without altering the underlying generator. It uses the Gemini-2.5-Flash model as a generic policy-violating concept detector, avoiding reliance on multiple category-specific detectors, and performs localized semantic editing to replace unsafe content. Prior post-hoc editing methods often rely on imprecise spatial localization, that undermines usability and limits deployability, particularly in multi-concept scenes. To address this limitation, ReVision introduces a VLM-assisted spatial gating mechanism that enforces instance-consistent localization, enabling precise edits while preserving scene integrity. We evaluate ReVision on a 245-image benchmark covering both single- and multi-concept scenarios. Results show that ReVision (i) improves CLIP-based alignment toward safe prompts by +$0.121$ on average; (ii) significantly improves multi-concept background fidelity (LPIPS $0.166 \rightarrow 0.058$); (iii) achieves near-complete suppression on category-specific detectors (e.g., NudeNet $70.51 \rightarrow 0$); and (iv) reduces policy-violating content recognizability in a human moderation study from $95.99\%$ to $10.16\%$.