title: Present and Future Generalization of Synthetic Image Detectors
summary: The continued release of new and better image generation models increases the
  demand for synthetic image detectors. In such a dynamic field, detectors need
  to be able to generalize widely and be robust to uncontrolled alterations. The
  present work is motivated by this setting, when looking at the role of time,
  image transformations and data sources, for detector generalization. In these
  experiments, none of the evaluated detectors is found universal, but results
  indicate an ensemble could be. Experiments on data collected in the wild show
  this task to be more challenging than the one defined by large-scale datasets,
  pointing to a gap between experimentation and actual practice. Finally, we
  observe a race equilibrium effect, where better generators lead to better
  detectors, and vice versa. We hypothesize this pushes the field towards a
  perpetually close race between generators and detectors.
paper_authors._id: 66f3cc15ec5d94d8cb43ada9
paper_authors.name: Pablo Bernabeu-Perez
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-09-25T09:05:43.094Z
paper_authors._id: 620683e7eeb1b73d904c96e5
paper_authors.avatarUrl: /avatars/d0309ac9408530a74f1799e175cc5fad.svg
paper_authors.fullname: Pablo Bernabeu
paper_authors.type: user
paper_authors.user: pabberpe
paper_authors._id: 66f3cc15ec5d94d8cb43adaa
paper_authors.name: Enrique Lopez-Cuena
paper_authors._id: 66f3cc15ec5d94d8cb43adab
paper_authors.name: Dario Garcia-Gasulla
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-09-25T08:50:20.388Z
paper_authors._id: 62f7a16192950415b637e201
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62f7a16192950415b637e201/4IIqYap43vujvSuql68Vj.jpeg
paper_authors.fullname: Dario
paper_authors.type: user
paper_authors.user: dariog
paper_title: Present and Future Generalization of Synthetic Image Detectors
paper_summary: The continued release of new and better image generation models increases the
  demand for synthetic image detectors. In such a dynamic field, detectors need
  to be able to generalize widely and be robust to uncontrolled alterations. The
  present work is motivated by this setting, when looking at the role of time,
  image transformations and data sources, for detector generalization. In these
  experiments, none of the evaluated detectors is found universal, but results
  indicate an ensemble could be. Experiments on data collected in the wild show
  this task to be more challenging than the one defined by large-scale datasets,
  pointing to a gap between experimentation and actual practice. Finally, we
  observe a race equilibrium effect, where better generators lead to better
  detectors, and vice versa. We hypothesize this pushes the field towards a
  perpetually close race between generators and detectors.
paper_ai_summary: Synthetic image detectors must generalize widely and resist alterations in a rapidly evolving field, where the improvement of generators drives advancements in detectors and vice versa.
paper_ai_keywords: image generation models synthetic image detectors detector generalization image transformations race equilibrium effect