DIAGNOSIS: Detecting Unauthorized Data Usages in Text-to-image Diffusion Models
Authors: Zhenting Wang, Chen Chen, Lingjuan Lyu, Dimitris N. Metaxas, Shiqing Ma
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on Stable Diffusion and VQ Diffusion with different model training or fine-tuning methods (i.e, Lo RA, Dream Booth, and standard training) demonstrate the effectiveness of our proposed method in detecting unauthorized data usages. |
| Researcher Affiliation | Collaboration | 1Rutgers University, 2Sony AI, 3University of Massachusetts Amherst |
| Pseudocode | Yes | Algorithm 1 Data Coating and Algorithm 2 Unauthorized Data Usages Detection are provided as pseudocode blocks. |
| Open Source Code | Yes | Code: https://github.com/Zhenting Wang/DIAGNOSIS. |
| Open Datasets | Yes | Four datasets (i.e., Pokemon3, Celeb A (Liu et al., 2015), CUB-200 (Wah et al., 2011)) and Dog (Ruiz et al., 2023) are used. |
| Dataset Splits | Yes | Note that we spit part of the samples (10% by default) in D as the validation set for developing the signal classifier. |
| Hardware Specification | Yes | We conduct all experiments on a Ubuntu 20.04 server equipped with six Quadro RTX 6000 GPUs. |
| Software Dependencies | Yes | Our method is implemented with Python 3.9 and Py Torch 2.0.1. |
| Experiment Setup | Yes | By default, the coating rate we used for unconditional injected memorization and the trigger-conditioned injected memorization are 100.0% and 20.0%, respectively. We use 50 text prompts to approximate the memorization strength (i.e., N = 50) by default. The default warping strength are 2.0 and 1.0 for unconditional injected memorization and trigger-conditioned injected memorization, respectively. The default hyper-parameters for the hypothesis testing are discussed in 3.3. |