  Over the past decade, there has been tremendous progress in creating
synthetic media, mainly thanks to the development of powerful methods based on
generative adversarial networks (GAN). Very recently, methods based on
diffusion models (DM) have been gaining the spotlight. In addition to providing
an impressive level of photorealism, they enable the creation of text-based
visual content, opening up new and exciting opportunities in many different
application fields, from arts to video games. On the other hand, this property
is an additional asset in the hands of malicious users, who can generate and
distribute fake media perfectly adapted to their attacks, posing new challenges
to the media forensic community. With this work, we seek to understand how
difficult it is to distinguish synthetic images generated by diffusion models
from pristine ones and whether current state-of-the-art detectors are suitable
for the task. To this end, first we expose the forensics traces left by
diffusion models, then study how current detectors, developed for GAN-generated
images, perform on these new synthetic images, especially in challenging
social-networks scenarios involving image compression and resizing. Datasets
and code are available at github.com/grip-unina/DMimageDetection.
