title: ImagiNet: A Multi-Content Dataset for Generalizable Synthetic Image
    Detection via Contrastive Learning
summary: 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.
paper_authors._id: 66a8ceaf63160e558400c6d2
paper_authors.name: Delyan Boychev
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-02-18T19:39:36.912Z
paper_authors._id: 63de1f5c15266dd945ef9111
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1675501270851-noauth.jpeg
paper_authors.fullname: Delyan Boychev
paper_authors.type: user
paper_authors.user: delyanboychev
paper_authors._id: 66a8ceaf63160e558400c6d3
paper_authors.name: Radostin Cholakov
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-07-30T11:30:11.105Z
paper_authors._id: 629bbadee1af194c64236565
paper_authors.avatarUrl: /avatars/2297243fae6456e031e9c3ff6997e8b2.svg
paper_authors.fullname: Radostin Cholakov
paper_authors.type: user
paper_authors.user: radi-cho
paper_title: ImagiNet: A Multi-Content Dataset for Generalizable Synthetic Image
    Detection via Contrastive Learning
paper_summary: 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.
paper_ai_summary: ImagiNet, a high-resolution dataset, aids in synthetic image detection using ResNet-50 trained with SelfCon, achieving high accuracy under various conditions.
paper_ai_keywords: diffusion models variational autoencoders generative adversarial networks ImagiNet synthetic image detection ResNet-50 contrastive objective SelfCon AUC balanced accuracy