The unit for information security and IT architecture at The Swedish Defence Research Agency (FOI) conducts work with a cyber range called CRATE (Cyber Range and Training Environment). Currently, simulation of user activity involves scripts inside the simulated network. This solution is not ideal because of the traces it leaves in the system and the general lack of standardised GUI API between different operating systems. FOI are interested in testing the use of artificial user agent located outside the virtual environment using computer vision and the virtualisation API to execute actions and extract information from the system. This paper focuses on analysing the reliability of template matching, a computer vision algorithm used to localise objects in images using already identified images of said object as templates. The analysis will evaluate both the reliability of localising objects and the algorithms ability to correctly identify if an object is present in the virtual environment. Analysis of template matching is performed by first creating a prototype of the agent's sensory system and then simulate scenarios which the agent might encounter. By simulating the environment, testing parameters can be manipulated and monitored in a reliable way. The parameters manipulated involves both the amount and type of image noise in the template and screenshot, the agent’s discrimination threshold for what constitutes a positive match, and information about the template such as template generality. This paper presents the performance and reliability of the agent in regards to what type of image noise affects the result, the amount of correctly identified objects given different discrimination thresholds, and computational time of template matching when different image filters are applied. Furthermore the best cases for each study are presented as comparison for the other results. In the end of the thesis we present how for screenshots with objects very similar to the templates used by the agent, template matching can result in a high degree of accuracy in both object localization and object identification and that a small reduction of similarity between template and screenshot to reduce the agent's ability to reliably identifying specific objects in the environment.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-129909 |
| Date | January 2016 |
| Creators | Estgren, Martin |
| Publisher | Linköpings universitet, Artificiell intelligens och integrerad datorsystem |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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