J. ICT Res. Appl., Vol. 17, No. 2, 2023, 181-200 181 Received August 23 rd , 2022, 1 st Revision March 4 th , 2023, 2 nd Revision June 15 th , 2023, Accepted for publication July 21 st , 2023. Copyright © 2023 Published by IRCS-ITB, ISSN: 2337-5787, DOI: 10.5614/itbj.ict.res.appl.2023.17.2.4 Generative Adversarial Networks Based Scene Generation on Indian Driving Dataset K. Aditya Shastry*, B.A. Manjunatha, M. Mohan, T.G. Mohan Kumar & D.U. Karthik Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bengaluru, 560064, India *E-mail: adityashastry.k@nmit.ac.in Abstract. The rate of advancement in the field of artificial intelligence (AI) has drastically increased over the past twenty years or so. From AI models that can classify every object in an image to realistic chatbots, the signs of progress can be found in all fields. This work focused on tackling a relatively new problem in the current scenario-generative capabilities of AI. While the classification and prediction models have matured and entered the mass market across the globe, generation through AI is still in its initial stages. Generative tasks consist of an AI model learning the features of a given input and using these learned values to generate completely new output values that were not originally part of the input dataset. The most common input type given to generative models are images. The most popular architectures for generative models are autoencoders and generative adversarial networks (GANs). Our study aimed to use GANs to generate realistic images from a purely semantic representation of a scene. While our model can be used on any kind of scene, we used the Indian Driving Dataset to train our model. Through this work, we could arrive at answers to the following questions: (1) the scope of GANs in interpreting and understanding textures and variables in complex scenes; (2) the application of such a model in the field of gaming and virtual reality; (3) the possible impact of generating realistic deep fakes on society. Keywords: artificial intelligence; deep learning; driving dataset; generative adversial networks; scene generation. 1 Introduction The advent of artificial intelligence began in 1943 when the first paper on the concept of a neural network was published. Progress in AI has come a long way, from a theoretical neural network model to models that can detect, classify, predict, and more recently, generate data they have been trained on. The internet has been abuzz with deep fakes and the potential issues such technology could cause with the advent of generative models. However, generative models provide a twofold benefit: they can generate new data in areas lacking suitable data (such as native language processing) and they can be used to interpret and analyze already existing data.