  Recently proposed large-scale text-to-image generative models such as
DALL$\cdot$E 2, Midjourney, and StableDiffusion can generate high-quality and
realistic images from users' prompts. Not limited to the research community,
ordinary Internet users enjoy these generative models, and consequently, a
tremendous amount of generated images have been shared on the Internet.
Meanwhile, today's success of deep learning in the computer vision field owes a
lot to images collected from the Internet. These trends lead us to a research
question: "\textbf{will such generated images impact the quality of future
datasets and the performance of computer vision models positively or
negatively?}" This paper empirically answers this question by simulating
contamination. Namely, we generate ImageNet-scale and COCO-scale datasets using
a state-of-the-art generative model and evaluate models trained with
"contaminated" datasets on various tasks, including image classification and
image generation. Throughout experiments, we conclude that generated images
negatively affect downstream performance, while the significance depends on
tasks and the amount of generated images. The generated datasets and the codes
for experiments will be publicly released for future research. Generated
datasets and source codes are available from
\url{https://github.com/moskomule/dataset-contamination}.
