Generative AI poses interesting challenges for academic publishers tackling fraud in science papers as the technology shows the potential to fool human peer review.

Describe an image for DALL-E, Stable Diffusion, and Midjourney, and they’ll generate one in seconds. These text-to-picture systems have rapidly improved over the past few years, and what initially began as a research prototype, producing benign and wonderfully bizarre illustrations of baby daikon radishes walking dogs in 2021, has since morphed into commercial software, built by billion-dollar companies, capable of generating increasingly realistic images.

These AI models can produce lifelike pictures of human faces, objects, and scenes, and it’s a matter of time before they get good at creating convincing scientific images and data, too. Text-to-image models are now widely accessible, pretty cheap to use, and they could help dodgy scientists forge results and publish sham research more easily.

Image manipulation is already a top concern for academic publishers as it’s the most common form of scientific misconduct of late. Authors can use all sorts of tricks, such as flipping, rotating, or cropping parts of the same image to fake findings. Editors are fooled into believing the results being presented are real and will publish the work.