发表机构
Institute of Data Science and Information Computing, National Chung Hsing University; National Taiwan University of Science and Technology（国立中兴大学数据科学与信息计算研究所; 台湾科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对WB图像伪造取证缺乏可控生成基准的问题，提出GAN-Blot框架，通过结构-风格分解与双路径自编码实现高保真合成，并贡献超4.6万图像数据集及四种评估协议，生成的图像可欺骗专家和现有检测器。
AI 中文摘要
蛋白质印迹（WB）图像在生物医学研究中被广泛用作关键证据。近年来的科研不端案例显示，WB图像被伪造的情况日益增多，使得WB取证成为研究诚信领域的一个主要关注点。然而，尽管取证检测技术的进步往往依赖于伪造生成技术的进步，但WB取证技术的发展却一直受到缺乏标准化外观属性定义、图像数据集以及可控WB图像生成框架的阻碍。为解决这一局限，我们提出了一种名为GAN-Blot的可控WB图像合成框架，用于生成逼真的合成WB图像。我们引入了一种将WB图像分解为结构分量和风格参考分量的公式，从而能够独立控制合成WB图像的局部蛋白条带几何形状和全局视觉外观。GAN-Blot集成了双路径自编码设计以及多种风格对齐损失项，无需预定义的语义外观属性即可实现对结构-风格合成的隐式控制。我们进一步贡献了一个包含超过46,000张图像的合成WB数据集，并提出了四种用于可控WB合成的评估协议。大量实验表明，GAN-Blot能够在蛋白条带结构和视觉风格方面生成高保真度的WB图像。在盲检条件下，生成的图像能够欺骗领域专家，并且现有检测器和筛选平台无法可靠地将其与真实WB图像区分开来。这些结果证明了它们作为具有挑战性的受控案例，可用于验证和开发WB取证方法的实用性。
英文摘要
Western blot (WB) images are widely used as key evidence in biomedical research. Recent scientific misconduct cases reveal that WB imagery is increasingly fabricated, making WB forensics a major concern for research integrity. However, while the progress of forensic detection techniques often relies on advances in forgery-generation techniques, the development of WB forensic techniques has been hindered by the lack of standardized appearance attribute definitions, image datasets, and controllable generation frameworks for WB imagery. To address this limitation, we present a controllable WB image synthesis framework, named GAN-Blot, for generating realistic synthetic WB images. We introduce a formulation that decomposes a WB image into a structure component and a style-reference component, enabling independent control over local protein-band geometry and the global visual appearance of a synthetic WB image. GAN-Blot integrates a dual-path autoencoding design with several style-alignment loss terms to enable implicit control over structure-style synthesis without predefined semantic appearance attributes. We further contribute a synthetic WB dataset containing more than 46K images and propose four evaluation protocols for controllable WB synthesis. Extensive experiments show that GAN-Blot can generate WB images with high fidelity in both protein-band structure and visual style. Under blind inspection, the generated images can fool domain experts and are not reliably distinguished from authentic WB images by existing detectors and screening platforms. These results demonstrate their utility as challenging controlled cases for validating and developing WB forensic methods.
