GuardFlow: Image Immunization via Trajectory Disruption against Flow-Based Image Editing

Text-guided image editing models enable powerful manipulation of visual content but also introduce serious security risks by allowing unauthorized or malicious edits. Image immunization aims to protect images by adding imperceptible perturbations that induce edit failure. However, existing methods are primarily designed for GAN or diffusion models and do not generalize to flow-based generative systems. In this work, we propose GuardFlow, the first immunization framework that guards images from flow-based editing models. Our key insight is to exploit the trajectory-driven nature of flow models, where small perturbations accumulate and alter the generation path. We further derive a theoretical perturbation bound that ensures effective disruption while preserving perceptual quality. Experiments on major flow-based editing frameworks show that GuardFlow effectively impedes text-guided editing while maintaining high visual fidelity, outperforming existing baselines in both effectiveness and robustness. The code will be released upon publication.