通过2万张图像精准识别生成模型，揭示统一模型的视觉指纹。
Guess the Unified Model: How Much Can We Recover from Generated Images?
- 用7个统一模型生成图像，测试模型溯源可行性
- 每模型约2万张图像时准确率达近完美
- 语言差异影响小，内容语义非主要区分信号
随着统一模型生成的图像在互联网上日益普及，识别其来源模型为提升透明度和理解各模型特征行为提供了新路径。现有研究已探索大语言模型文本、扩散模型图像及数据集的溯源问题，但对统一模型生成图像的可区分性仍研究不足。本文通过分析七种统一模型在不同噪声、领域和提示语言下的生成图像，评估其可分离性。结果表明，模型溯源高度可行：每个模型约2万张图像即可实现近乎完美的识别准确率。噪声干扰和结构扰动对溯源性能影响较小；跨域泛化显示，语义内容虽有助于区分，但并非主导信号。多数模型在提示语言溯源上仅达随机水平，说明语言差异未留下显著的视觉特征。这些发现揭示了统一模型输出中稳定存在的模型特异性视觉特征，为追踪与审计生成图像流水线开辟了新方向。
原文摘要 · Abstract (English)
With unified model-generated images now widespread online, attributing their model of origin offers a path toward transparency and deeper insight into the characteristic behaviors of individual models. Prior work has explored provenance in LLM-generated text, diffusion model images, and datasets, but the separability of unified model-generated images remains an underexplored area. We address this gap by examining separability across corruption, domains, and prompt languages using images generated by seven unified models. We show that model attribution is highly feasible as our model achieves near-perfect accuracy with around 20K images per model. Corruptions and structural perturbations have only a modest effect on attribution performance, and cross-domain generalization reveals that semantic content contributes to separability but is not the dominant signal. Finally, we observe that for most models, prompt language attribution is around chance levels, suggesting minimal language-specific visual signatures. These findings highlight consistent model-specific visual characteristics in unified models outputs and open new directions for tracing and auditing generative image pipelines.
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