| Summary: | In recent years, the popular network of adversarial networks has increased in studies for computer vision. The lack of data used
in the studies and the lack of good training for the resulting model draw attention to techniques such as data enhancement and
synthetic data generation. In this article, synthetic data was produced using Generative Adversarial Networks (GANs). The data
in the dataset used consists of 10000 faces from the CelebA dataset available online. The impact of the increase in the number
of data on fake images created by DCGAN, one of the GANs, is the main topic of the article. In the study, the data is divided
into two parts. In the first study, fake data were generated from 5000 data, and in the next study, fake data images were forged
using all of the data meaning 10000 data. The result was found that the number of data and the increase in epoch were accurately
proportional to the success of the fraudulent images created.
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