用隐蔽方式阻止恶意图像编辑，保持原图自然不露破绽。
NullEdit: Stealthy Image Protection via VLM Condition Redirection
- 通过重定向视觉语言模型的联合表征实现无害静默防护。
- 在CelebA-HQ和VGGFace2上使编辑奖励分数降低0.813。
- 适合需要保护图像不被篡改又不暴露防御痕迹的场景。
现代图像编辑器利用视觉语言模型（VLM）与扩散变换器（DiT）结合，在无需微调的情况下根据指令修改单张参考图像。这一能力也带来了对公开发布图像的未经授权操作风险。现有推理阶段防御要么通过明显破坏来失效编辑，暴露防护；要么允许编辑进行但导致身份或参考内容漂移，无法真正阻止编辑行为。本文提出NullEdit，实现一种隐蔽且无害的空操作：请求的编辑被抑制，输出保持自然且保留源图像特征，无明显伪影或身份替换，恶意语义也被消除。NullEdit针对参考图像与指令共同形成的VLM表示进行干预，使用正常编辑和无编辑锚点，通过跨提示梯度平均将保护能力泛化至未见指令。在Step1X-Edit和Qwen-Image-Edit框架下，基于CelebA-HQ和VGGFace2数据集测试，相比当前最优基线，平均使EditReward IF得分下降0.813，同时有效保留主体身份与源内容。
原文摘要 · Abstract (English)
Modern image editors combine vision-language models (VLMs) with diffusion transformer backbones to modify a single reference image according to instructions without fine-tuning. This capability also enables unauthorized manipulation of publicly released images. Existing inference-time defenses either invalidate edits through conspicuous corruption, thereby exposing the protection, or allow them to proceed with identity or reference content drift, thereby failing to prevent the editing behavior itself. We instead target a stealthy and harmless no-op in which the requested edit is suppressed, the output remains natural and source-preserving without conspicuous artifacts or identity replacement, and harmful semantics requested by malicious instructions are absent. We propose NullEdit, which targets the VLM representation jointly formed from the reference image and instruction before it conditions the downstream DiT backbone. Using normal-edit and no-edit anchors, NullEdit redirects this representation, while cross-prompt gradient averaging transfers protection to held out instructions. Across Step1X-Edit and Qwen-Image-Edit on CelebA-HQ and VGGFace2, NullEdit reduces the EditReward IF score by 0.813 on average relative to the SOTA baseline while preserving subject identity and source content.
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