title: Generalizable Origin Identification for Text-Guided Image-to-Image
    Diffusion Models
summary: Text-guided image-to-image diffusion models excel in translating images based
  on textual prompts, allowing for precise and creative visual modifications.
  However, such a powerful technique can be misused for spreading misinformation,
  infringing on copyrights, and evading content tracing. This motivates us to
  introduce the task of origin IDentification for text-guided Image-to-image
  Diffusion models (ID^2), aiming to retrieve the original image of a given
  translated query. A straightforward solution to ID^2 involves training a
  specialized deep embedding model to extract and compare features from both
  query and reference images. However, due to visual discrepancy across
  generations produced by different diffusion models, this similarity-based
  approach fails when training on images from one model and testing on those from
  another, limiting its effectiveness in real-world applications. To solve this
  challenge of the proposed ID^2 task, we contribute the first dataset and a
  theoretically guaranteed method, both emphasizing generalizability. The curated
  dataset, OriPID, contains abundant Origins and guided Prompts, which can be
  used to train and test potential IDentification models across various diffusion
  models. In the method section, we first prove the existence of a linear
  transformation that minimizes the distance between the pre-trained Variational
  Autoencoder (VAE) embeddings of generated samples and their origins.
  Subsequently, it is demonstrated that such a simple linear transformation can
  be generalized across different diffusion models. Experimental results show
  that the proposed method achieves satisfying generalization performance,
  significantly surpassing similarity-based methods (+31.6% mAP), even those
  with generalization designs.
paper_authors._id: 677f28c33d2765f5655e948f
paper_authors.name: Wenhao Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-13T08:54:38.907Z
paper_authors._id: 62b32a4429a410b7f6b06710
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62b32a4429a410b7f6b06710/VzgvmnlYZWuifZTkIkCxy.jpeg
paper_authors.fullname: Wenhao Wang
paper_authors.type: user
paper_authors.user: WenhaoWang
paper_authors._id: 677f28c33d2765f5655e9490
paper_authors.name: Yifan Sun
paper_authors._id: 677f28c33d2765f5655e9491
paper_authors.name: Zongxin Yang
paper_authors._id: 677f28c33d2765f5655e9492
paper_authors.name: Zhentao Tan
paper_authors._id: 677f28c33d2765f5655e9493
paper_authors.name: Zhengdong Hu
paper_authors._id: 677f28c33d2765f5655e9494
paper_authors.name: Yi Yang
paper_title: Generalizable Origin Identification for Text-Guided Image-to-Image
    Diffusion Models
paper_summary: Text-guided image-to-image diffusion models excel in translating images based
  on textual prompts, allowing for precise and creative visual modifications.
  However, such a powerful technique can be misused for spreading misinformation,
  infringing on copyrights, and evading content tracing. This motivates us to
  introduce the task of origin IDentification for text-guided Image-to-image
  Diffusion models (ID^2), aiming to retrieve the original image of a given
  translated query. A straightforward solution to ID^2 involves training a
  specialized deep embedding model to extract and compare features from both
  query and reference images. However, due to visual discrepancy across
  generations produced by different diffusion models, this similarity-based
  approach fails when training on images from one model and testing on those from
  another, limiting its effectiveness in real-world applications. To solve this
  challenge of the proposed ID^2 task, we contribute the first dataset and a
  theoretically guaranteed method, both emphasizing generalizability. The curated
  dataset, OriPID, contains abundant Origins and guided Prompts, which can be
  used to train and test potential IDentification models across various diffusion
  models. In the method section, we first prove the existence of a linear
  transformation that minimizes the distance between the pre-trained Variational
  Autoencoder (VAE) embeddings of generated samples and their origins.
  Subsequently, it is demonstrated that such a simple linear transformation can
  be generalized across different diffusion models. Experimental results show
  that the proposed method achieves satisfying generalization performance,
  significantly surpassing similarity-based methods (+31.6% mAP), even those
  with generalization designs.
paper_ai_keywords: diffusion models text-guided image-to-image diffusion models ID$^2$ deep embedding model Variational Autoencoder (VAE) OriPID linear transformation