Robust Source Attribution of Synthetically Generated Western Blot Images

Abstract

Retracted papers commonly include manipulations of images and figures that are unfit for publication. While some manipulations are benign, like increasing contrast or zooming in, others are designed to fool the intended audience. Recent improvements in generative computer vision pose a security risk to scientific review since generated images are often indistinguishable from authentic bioscience evidence; even to field experts. In this work, we improve upon previous attempts to detect synthetic images by attending to their differences in the frequency domain. Additionally, we solve the multi- class classification of synthetic Western blots and attribute Western blot images to their respective generative model architecture. We demonstrate that our method outperforms previous methods for synthetic Western blot detection; including efforts to classify JPEG compressed images. Please note that this is a technical report based on preliminary research results