AI 中文总结
研究统一多模态模型中个人肖像编辑的安全问题，提出CCS统一对抗保护框架，通过联合驱动ViT和VAE表示及破坏跨分支兼容性，防止模型恢复身份信息，在抑制身份保留编辑上表现优于现有方法。
AI 中文摘要
统一多模态模型（UMMs）展示了强大的基于指令的图像编辑能力，但引发了对个人肖像未经授权操纵的担忧。现有对抗保护方法不适用于UMMs。本文首先对统一图像编辑进行特征级分析，发现基于ViT的理解分支和基于VAE的生成分支对同一输入图像有结构一致性，单分支特征失真不足以破坏统一图像编辑。因此提出CCS框架，通过线性CKA联合驱动ViT和VAE表示远离干净对应物并破坏跨分支兼容性，防止UMMs在编辑时恢复可靠身份信息。实验表明CCS在抑制身份保留编辑方面优于现有方法。
英文摘要
Unified multimodal models (UMMs) have recently demonstrated powerful instruction-based image editing capabilities, while also raising serious concerns about the unauthorized manipulation of personal portraits. We investigate a novel and practical problem: protecting facial identities against unauthorized editing of UMMs. Existing diffusion-based and VLM-based protection methods often become ineffective because they typically disrupt only a single visual branch. To understand this limitation, we conduct a feature-level analysis of the understanding and generation branches in unified image editing models. Our observations show that the structural agreement between these two branches is closely related to successful image editing. When only one branch is distorted, the model may still recover identity information from the other branch. Based on this, we propose Cross-Branch Conflict as a Shield (CCS), a unified adversarial protection framework. CCS jointly drives the ViT and VAE representations away from their clean counterparts. It also uses a linear Centered Kernel Alignment (CKA) objective to disrupt the structural consistency between the two branches. By degrading reliable identity information in both visual pathways and inducing incompatible cross-branch representations, CCS effectively prevents UMMs from recovering consistent facial identity cues during editing. Extensive experiments suggest that CCS consistently provides stronger protection in suppressing identity-preserving edits. Codes are in the supplementary material.
Comments14 pages, preprint
