Image Copy Detection for Diffusion Models
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%.
66fb85166ac3a124bde6742a
Wenhao Wang
admin_assigned
2024-10-01T08:54:18.519Z
62b32a4429a410b7f6b06710
https://cdn-avatars.huggingface.co/v1/production/uploads/62b32a4429a410b7f6b06710/VzgvmnlYZWuifZTkIkCxy.jpeg
Wenhao Wang
user
WenhaoWang
66fb85166ac3a124bde6742b
Yifan Sun
66fb85166ac3a124bde6742c
Zhentao Tan
66fb85166ac3a124bde6742d
Yi Yang
Image Copy Detection for Diffusion Models
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%.
PDF-Embedding is a novel deep embedding method for detecting content replication in images generated by diffusion models, achieving superior performance on a new D-Rep dataset compared to existing methods.
diffusion models Image Copy Detection D-Rep dataset PDF-Embedding probability density function replication levels Stable Diffusion V1.5