Image Copy Detection for Diffusion Models
Authors: Wenhao Wang, Yifan Sun, Zhentao Tan, Yi Yang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that PDF-Embedding surpasses protocol-driven methods and non-PDF choices on the D-Rep test set. |
| Researcher Affiliation | Collaboration | Wenhao Wang1, Yifan Sun2 , Zhentao Tan2, Yi Yang3 1University of Technology Sydney 2Baidu Inc. 3Zhejiang University |
| Pseudocode | No | The paper describes its method with mathematical formulas and textual explanations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The project is publicly available at https://icdiff.github.io/. |
| Open Datasets | Yes | A Diffusion Replication (D-Rep) dataset. D-Rep consists of 40, 000 image-replica pairs, in which each replica is generated by a diffusion model. Specifically, the images are from LAION-Aesthetic V2 [1], while their replicas are generated by Stable Diffusion V1.5 [12]. The project is publicly available at https://icdiff.github.io/. |
| Dataset Splits | No | The paper states: 'We divide D-Rep into a training set with 90% (36, 000) pairs and a test set with the remaining 10% (4, 000) pairs.' It mentions a training set and a test set but does not explicitly specify a separate validation set split or its size. |
| Hardware Specification | Yes | We implement our PDF-Embedding using Py Torch [38] and distribute its training over 8 A100 GPUs. |
| Software Dependencies | No | The paper mentions 'Py Torch [38]' and uses 'Vi T-B/16 [37]' as backbone and 'Dei T [40]' for pre-training. However, it does not specify version numbers for any of these software components. |
| Experiment Setup | Yes | We resize images to a resolution of 224 224 before training. A batch size of 512 is used, and the total training epochs is 25 with a cosine-decreasing learning rate. |