01 / Key message
VETO: Towards Protecting Images From Frontier AI Editing
A subtle image cloak that disrupts how modern unified editors attend to a protected reference image.
Reference attention shield
02 / Method
How it works
Read the source
Track where canvas tokens retrieve information from protected reference-image tokens.
Diffuse attention
Optimize a subtle image perturbation that maximizes entropy across the reference-canvas attention blocks.
Break faithful editing
The editor can no longer preserve the source reliably, while the protected image remains visually close to the original.
03 / Abstract
Abstract
Frontier image editors such as FLUX.2 can move identities and objects into entirely new scenes, extending misuse beyond predictable localized edits. Existing anti-edit defenses target the encoder bottleneck used by legacy diffusion pipelines, but unified editors repeatedly access source-image tokens through joint attention. VETO instead optimizes a subtle per-image cloak that maximizes the entropy of canvas-to-reference and reference-to-canvas attention, disrupting source information as it flows into the generated output. The accompanying VetoBench evaluates both conventional closed-frame edits and open-frame recontextualization across general, defamatory, and graphic scenarios.
04 / Contributions
What this adds
- 01
Attention-level protection
Targets the joint-attention mechanism used by native DiT editors instead of attacking a legacy encoder bottleneck.
- 02
Stronger protection-fidelity trade-off
Consistently leaves fewer successful edits while preserving more of the protected image than prior cloaking methods.
- 03
VetoBench
Adds 300 curated cases spanning closed-frame edits and open-frame recontextualization across general, defamatory, and graphic scenarios.
05 / Citation
Citation
Grebe, J., Shakibania, H., Braun, T., Rohrbach, M., & Rohrbach, A. (2026). VETO: Towards Protecting Images From Frontier AI Editing. Preprint.
BibTeX
@misc{grebe2026veto,
title = {{VETO}: Towards Protecting Images From Frontier AI Editing},
author = {Jonas Grebe and Hossein Shakibania and Tobias Braun and Marcus Rohrbach and Anna Rohrbach},
year = {2026},
note = {Preprint}
}