Diffusion Illusions: Hiding Images in Plain Sight
Ryan Burgert Xiang Li Abe Leite Kanchana Ranasinghe Michael S. Ryoo
We explore the problem of computationally generating special `prime' images
that produce optical illusions when physically arranged and viewed in a certain
way. 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 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. Our
code and examples are publicly available at our interactive project website:
https://diffusionillusions.com