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%. The project is publicly available at https://icdiff.github.io/.

翻译：扩散模型生成的图像在数字艺术品和视觉营销中日益普及。然而，此类生成图像可能复制现有图像的内容，从而引发内容原创性的挑战。现有的图像复制检测模型虽然在检测手工制作的复制品方面准确，却忽视了扩散模型带来的挑战。这促使我们提出ICDiff，首个专为扩散模型设计的图像复制检测方法。为此，我们构建了扩散复制数据集，并相应提出了一种新颖的深度嵌入方法。D-Rep使用最先进的扩散模型生成40,000个图像-复制品对，并人工标注为6个复制级别，范围从0到5。我们的方法，PDF-嵌入，将每个图像-复制品对的复制级别转换为概率密度函数作为监督信号。其直觉在于相邻复制级别的概率应当是连续且平滑的。实验结果表明，PDF-嵌入在D-Rep测试集上超越了协议驱动的方法和非PDF选择。此外，通过利用PDF-嵌入，我们发现知名扩散模型对开源图库的复制比率在10%至20%之间。该项目公开于https://icdiff.github.io/。