Using machine learning models to detect artificially generated images

Abstract

The use of machine learning models to classify real and artificially generated images is becoming an increasingly relevant area of research in the field of artificial intelligence. This paper is dedicated to the analysis and development of various machine learning models used for this task. Our main goal is to investigate the effectiveness of different neural network architectures in classifying images that have been created both in by human and with the help of artificial intelligence. As part of the study, we used nine different neural network architectures designed to analyse their effectiveness in classifying images. We studied both real images and those generated by artificial intelligence. The latter are becoming more and more common in the modern world, which means that there is a need for their identification and classification. The main results of our study include a detailed analysis of the characteristics of the generated images, as well as a comparison of the models using various metrics. We used metrics such as accuracy and F1-score, which allowed us to objectively evaluate the performance of each model. In addition, we identified the most effective models for the image classification task. One of the key findings of our study is that models that use data regularisation and augmentation performed better. Data regularisation and augmentation help to ensure more stable classification accuracy and reduce the tendency to overfitting, which is an important issue in machine learning. These results can be useful for developing strategies to counteract disinformation, which has become an issue in the modern information society. By using machine learning models, artificially generated images can be detected and separated from real images, which can help prevent the spread of fake news and disinformation. In addition, the results of our research can contribute to the further improvement of image generation algorithms, which is an important area of artificial intelligence development. They can serve as a basis for creating more accurate and efficient models that can generate images that are as close to real life as possible.