Explainable Artifacts for Synthetic Western Blot Source Attribution
It addresses the challenge of detecting AI-generated fraudulent scientific images to prevent misinformation, though it appears incremental by focusing on explainability over existing black-box methods.
This study tackled the problem of identifying synthetic scientific images, such as Western blots, by detecting explainable artifacts from generative models like GANs and diffusion models, enabling open-set identification and source attribution to combat misinformation from paper mills.
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).