title: Image Copy Detection for Diffusion Models
summary: Images produced by diffusion models are increasingly popular in digital
  artwork and visual marketing. However, such generated images might replicate
  content from existing ones and pose the challenge of content originality.
  Existing Image Copy Detection (ICD) models, though accurate in detecting
  hand-crafted replicas, overlook the challenge from diffusion models. This
  motivates us to introduce ICDiff, the first ICD specialized for diffusion
  models. To this end, we construct a Diffusion-Replication (D-Rep) dataset and
  correspondingly propose a novel deep embedding method. D-Rep uses a
  state-of-the-art diffusion model (Stable Diffusion V1.5) to generate 40, 000
  image-replica pairs, which are manually annotated into 6 replication levels
  ranging from 0 (no replication) to 5 (total replication). Our method,
  PDF-Embedding, transforms the replication level of each image-replica pair into
  a probability density function (PDF) as the supervision signal. The intuition
  is that the probability of neighboring replication levels should be continuous
  and smooth. Experimental results show that PDF-Embedding surpasses
  protocol-driven methods and non-PDF choices on the D-Rep test set. Moreover, by
  utilizing PDF-Embedding, we find that the replication ratios of well-known
  diffusion models against an open-source gallery range from 10% to 20%.
paper_authors._id: 66fb85166ac3a124bde6742a
paper_authors.name: Wenhao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T08:54:18.519Z
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: 66fb85166ac3a124bde6742b
paper_authors.name: Yifan Sun
paper_authors._id: 66fb85166ac3a124bde6742c
paper_authors.name: Zhentao Tan
paper_authors._id: 66fb85166ac3a124bde6742d
paper_authors.name: Yi Yang
paper_title: Image Copy Detection for Diffusion Models
paper_summary: Images produced by diffusion models are increasingly popular in digital
  artwork and visual marketing. However, such generated images might replicate
  content from existing ones and pose the challenge of content originality.
  Existing Image Copy Detection (ICD) models, though accurate in detecting
  hand-crafted replicas, overlook the challenge from diffusion models. This
  motivates us to introduce ICDiff, the first ICD specialized for diffusion
  models. To this end, we construct a Diffusion-Replication (D-Rep) dataset and
  correspondingly propose a novel deep embedding method. D-Rep uses a
  state-of-the-art diffusion model (Stable Diffusion V1.5) to generate 40, 000
  image-replica pairs, which are manually annotated into 6 replication levels
  ranging from 0 (no replication) to 5 (total replication). Our method,
  PDF-Embedding, transforms the replication level of each image-replica pair into
  a probability density function (PDF) as the supervision signal. The intuition
  is that the probability of neighboring replication levels should be continuous
  and smooth. Experimental results show that PDF-Embedding surpasses
  protocol-driven methods and non-PDF choices on the D-Rep test set. Moreover, by
  utilizing PDF-Embedding, we find that the replication ratios of well-known
  diffusion models against an open-source gallery range from 10% to 20%.
paper_ai_keywords: diffusion models Image Copy Detection (ICD) Diffusion-Replication (D-Rep) dataset Stable Diffusion V1.5 image-replica pairs probability density function (PDF)