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

2026-07-01 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

Anti-Prompt is an image protection approach that injects imperceptible perturbations into images to defend against text-guided Image-to-Video (I2V) generation, addressing significant copyright and privacy risks. The method exploits the empirical observation that I2V generation quality degrades without text guidance, specifically in motion realism, subject preservation, structural coherence, and temporal consistency. Anti-Prompt works by attenuating text-conditioned interactions during denoising while strengthening visual-only pathways. To systematically evaluate its effectiveness, the authors introduced a Video-LLM-assisted evaluation protocol for frame-grounded analysis of artifacts. Experiments on two representative I2V architectures demonstrated strong protection performance, improved efficiency, and cross-model transferability.

Key takeaway

For content creators concerned about unauthorized animation of their images, Anti-Prompt offers a crucial defense. By injecting imperceptible perturbations, your images can resist text-guided Image-to-Video generation, preventing structural failures and inconsistencies in generated videos. You should consider integrating such protection mechanisms to safeguard your digital assets against copyright and privacy risks in the evolving landscape of generative AI.

Key insights

Anti-Prompt protects images from text-guided I2V generation by exploiting model reliance on textual guidance.

Principles

• I2V quality degrades without text guidance.
• Attenuate text-conditioned interactions during denoising.
• Strengthen visual-only pathways for protection.

Method

Anti-Prompt injects imperceptible perturbations into an image to induce visible inconsistencies and structural failures in text-guided I2V generation by attenuating text-conditioned interactions and strengthening visual-only pathways during denoising.

In practice

• Apply imperceptible perturbations to images.
• Use Video-LLM for artifact analysis.
• Protect against I2V copyright risks.

Topics

• Image Protection
• Text-Guided Image-to-Video
• Generative AI Security
• Copyright Protection
• Video-LLM Evaluation
• Computer Vision

Best for: Research Scientist, CTO, AI Product Manager, AI Scientist, Computer Vision Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.