  Generative models, such as diffusion models (DMs), variational autoencoders
(VAEs), and generative adversarial networks (GANs), produce images with a level
of authenticity that makes them nearly indistinguishable from real photos and
artwork. While this capability is beneficial for many industries, the
difficulty of identifying synthetic images leaves online media platforms
vulnerable to impersonation and misinformation attempts. To support the
development of defensive methods, we introduce ImagiNet, a high-resolution and
balanced dataset for synthetic image detection, designed to mitigate potential
biases in existing resources. It contains 200K examples, spanning four content
categories: photos, paintings, faces, and uncategorized. Synthetic images are
produced with open-source and proprietary generators, whereas real counterparts
of the same content type are collected from public datasets. The structure of
ImagiNet allows for a two-track evaluation system: i) classification as real or
synthetic and ii) identification of the generative model. To establish a
baseline, we train a ResNet-50 model using a self-supervised contrastive
objective (SelfCon) for each track. The model demonstrates state-of-the-art
performance and high inference speed across established benchmarks, achieving
an AUC of up to 0.99 and balanced accuracy ranging from 86% to 95%, even under
social network conditions that involve compression and resizing. Our data and
code are available at https://github.com/delyan-boychev/imaginet.
