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Diffusion Illusions: Hiding Images in Plain Sight

  • Ryan Burgert
  • , Xiang Li
  • , Abe Leite
  • , Kanchana Ranasinghe
  • , Michael Ryoo
  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We explore the problem of computationally generating special images that produce multi-arrangement optical illusions when physically arranged and viewed in a certain way, which we call 'prime' images. First, we propose a formal definition for this problem. Next, we introduce Diffusion Illusions, the first comprehensive pipeline designed to automatically generate a wide range of these multi-arrangement illusions. Specifically, we both adapt the existing 'score distillation loss' and propose a new 'dream target loss' to optimize a group of differentially parametrized prime images, using a frozen text-to-image diffusion model. We study three types of illusions, each where the prime images are arranged in different ways and optimized using the aforementioned losses such that images derived from them align with user-chosen text prompts or images. We conduct comprehensive experiments on these illusions and verify the effectiveness of our proposed method qualitatively and quantitatively. Additionally, we showcase the successful physical fabrication of our illusions - as they are all designed to work in the real world.

Original languageEnglish
Title of host publicationProceedings - SIGGRAPH 2024 Conference Papers
EditorsStephen N. Spencer
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400705250
DOIs
StatePublished - Jul 13 2024
Event2024 Special Interest Group on Computer Graphics and Interactive Techniques Conference - Conference Papers, SIGGRAPH 2024 - Denver, United States
Duration: Jul 28 2024Aug 1 2024

Publication series

NameProceedings - SIGGRAPH 2024 Conference Papers

Conference

Conference2024 Special Interest Group on Computer Graphics and Interactive Techniques Conference - Conference Papers, SIGGRAPH 2024
Country/TerritoryUnited States
CityDenver
Period07/28/2408/1/24

Keywords

  • ambiguous images
  • computational illusions
  • computer graphics
  • computer vision
  • diffusion
  • diffusion models
  • fourier feature networks
  • generative models
  • illusion generation
  • image generation
  • optical illusions
  • optimization
  • parametric image synthesis
  • perception
  • stenography
  • text-to-image synthesis
  • visual perception

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