Under CopyrightSeferis, EmmanouilEmmanouilSeferisBurton, SimonSimonBurtonCheng, Chih-HongChih-HongCheng2023-06-152023-06-152023https://publica.fraunhofer.de/handle/publica/442846https://doi.org/10.24406/publica-147310.24406/publica-1473Generative models that produce images, text, or other types of data are recently be equipped with more powerful capabilities. Nevertheless, in some use cases of the generated data (e.g., using it for model training), one must ensure that the synthetic data points satisfy some properties that make them suitable for the intended use. Towards this goal, we present a simple framework to statistically check if the data produced by a generative model satisfy some property with a given confidence level. We apply our methodology to standard image and text-to-image generative models.engenerative modelstatistical testingdeep neural networksDNNpresentation