Unmasking generative AI : A look at synthetic faces - Kuska, Petr; Chudán, David; Zamazal, Ondřej; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2023)
This thesis describes the foundations of generative artificial intelligence with a detailed explanation of three generative models – variational autoencoders, generative adversarial networks and diffusion models – and their applications. Furthermore, it discusses the risks and challenges of generative models and the proposed regulation of AI worldwide. It also investigates whether people can identify AI-generated images of faces. An online survey (N = 333) was conducted to test people's ability to distinguish between real and fake faces. The average accuracy of participants was 59.9% (95% confidence interval [58.1%, 61.8%]), which is above chance performance of 50% and is in line with previous research. The analysis showed that having experience with generative AI tools improved the detection ability with a statistically significant difference (p < 0.01) in the means of the inexperienced and experienced samples. The analysis showed no significant difference in accuracy across various screen sizes on which the participants viewed the faces. Seven online tools for detecting AI-generated images were tested, identifying one high-performing tool. In another experiment, three automated machine learning models were trained to classify real and AI-generated faces, which showed that although progress has been made in AutoML image classification, the models were not robust enough and underperformed the best online tool. The survey data and more can be found at https://github.com/mrpekus/master-thesis.
Vedoucí práce: David Chudán
Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2024
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2024
Způsob přístupu: Internet
Keywords: znalostní a webové technologie [obor dipl. práce]; diplomové práce; master's theses