Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |