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Counterterrorism for Cyber-Physical Spaces: A Computer Vision Approach
2020
Zenodo
Simulating terrorist scenarios in cyber-physical spaces---that is, urban open or (semi-) closed spaces combined with cyber-physical systems counterparts---is challenging given the context and variables therein. This paper addresses the aforementioned issue with ALTer a framework featuring computer vision and Generative Adversarial Neural Networks (GANs) over terrorist scenarios. We obtained the data for the terrorist scenarios by creating a synthetic dataset, exploiting the Grand Theft Auto V
doi:10.5281/zenodo.4534149
fatcat:luhzeaydvfempc5foaqxhhsyay