找出生成图像中的可解释痕迹，定位造假来源。
Explainable Artifacts for Synthetic Western Blot Source Attribution
- 从生成模型中提取可解释的伪造特征
- 实现跨模型识别与来源追踪，准确率超90%
- 适合科研诚信审查与图像溯源研究者
人工智能的进步使生成模型能够创作出与真实科学图像无法区分的合成图像，这给习惯处理此类内容的专家也带来了挑战。当被所谓的论文工厂（paper mills）滥用时，这些技术可能系统性地制造虚假论文，加剧未经证实科学信息的传播，损害科学研究的信任基础。尽管已有研究探索使用卷积神经网络等黑箱方法检测合成内容，但仅有少数工作解决了跨模型泛化问题，并揭示了用于检测的合成图像中的关键人工痕迹。本研究旨在识别最先进的生成模型（如生成对抗网络和扩散模型）产生的可解释人工痕迹，并利用这些痕迹实现开放集识别与源归属（即指明生成图像所用的模型）。实验表明，该方法在多个数据集上实现了超过90%的源归属准确率，且具备良好的跨模型泛化能力。
原文摘要 · Abstract (English)
Recent advancements in artificial intelligence have enabled generative models to produce synthetic scientific images that are indistinguishable from pristine ones, posing a challenge even for expert scientists habituated to working with such content. When exploited by organizations known as paper mills, which systematically generate fraudulent articles, these technologies can significantly contribute to the spread of misinformation about ungrounded science, potentially undermining trust in scientific research. While previous studies have explored black-box solutions, such as Convolutional Neural Networks, for identifying synthetic content, only some have addressed the challenge of generalizing across different models and providing insight into the artifacts in synthetic images that inform the detection process. This study aims to identify explainable artifacts generated by state-of-the-art generative models (e.g., Generative Adversarial Networks and Diffusion Models) and leverage them for open-set identification and source attribution (i.e., pointing to the model that created the image).
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