Anti-Prompt: Image Protection against Text-Guided Image-to-Video Generation

Recent advances in Image-to-Video (I2V) generation allow asingle image to be animated into a convincing video under text guidance,raising serious copyright and privacy risks. We propose Anti-Prompt,an image protection approach that injects imperceptible perturbationsinto an image, inducing visible inconsistencies and structural failures intext-guided I2V generation. Our method is motivated by a simple em-pirical observation: when text guidance is removed from modern I2Vmodels, generation quality degrades markedly, not only in motion re-alism but also in subject preservation, structural coherence, and tem-poral consistency. Building on this insight, Anti-Prompt exploits themodel’s reliance on textual guidance by attenuating text-conditioned in-teractions during denoising while strengthening visual-only pathways.To further systematically evaluate protection effectiveness, we introducea Video-LLM–assisted evaluation protocol that provides interpretable,frame-grounded analyses of generation artifacts and inconsistencies. Ex-periments on two representative I2V architectures demonstrate that ourmethod achieves strong protection performance while improving effi-ciency and cross-model transferability.