  The widespread diffusion of synthetically generated content is a serious
threat that needs urgent countermeasures. As a matter of fact, the generation
of synthetic content is not restricted to multimedia data like videos,
photographs or audio sequences, but covers a significantly vast area that can
include biological images as well, such as western blot and microscopic images.
In this paper, we focus on the detection of synthetically generated western
blot images. These images are largely explored in the biomedical literature and
it has been already shown they can be easily counterfeited with few hopes to
spot manipulations by visual inspection or by using standard forensics
detectors. To overcome the absence of publicly available data for this task, we
create a new dataset comprising more than 14K original western blot images and
24K synthetic western blot images, generated using four different
state-of-the-art generation methods. We investigate different strategies to
detect synthetic western blots, exploring binary classification methods as well
as one-class detectors. In both scenarios, we never exploit synthetic western
blot images at training stage. The achieved results show that synthetically
generated western blot images can be spot with good accuracy, even though the
exploited detectors are not optimized over synthetic versions of these
scientific images. We also test the robustness of the developed detectors
against post-processing operations commonly performed on scientific images,
showing that we can be robust to JPEG compression and that some generative
models are easily recognizable, despite the application of editing might alter
the artifacts they leave.
