Abstract
Movie Piracy is increasing these days, and it has a profound impact on the economic growth of film industries all over the world. Hence curbing piracy has become a critical step in avoiding massive losses to the film industry. This paper proposes a thermogram based anti-piracy system using Machine learning models. A local dataset is created by capturing the images in different scenarios by employing a thermal camera. AlexNet is used for extracting the features from captured images and the extracted features are trained with several Machine Learning models in MATLAB for their performance evaluation. The images captured in real-time are processed to extract the features and then directed to the classifier to predict the anomaly class. The alert message is sent to theatre officials regarding the presence of active recording device. An accuracy of 99.7% is achieved with Support Vector Machine (SVM) model on the local dataset.
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Funding
This research work is funded (GRD-907) by the Karnataka Science and Technology Promotion Society, Department of Information Technology, Biotechnology and Science & Technology, Government of Karnataka, India under the category of Research Grant for Scientist/Faculty (RGS/F), Vision Group of Science and Technology (VGST) Scheme.
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Sanjana, B., Sirisha, M., Dinesh, P., Kumar, G.R.S., Borra, S. (2022). Real-Time Piracy Detection Based on Thermogram Analysis and Machine Learning Techniques. In: Mukhopadhyay, S., Sarkar, S., Dutta, P., Mandal, J.K., Roy, S. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2022. Communications in Computer and Information Science, vol 1579. Springer, Cham. https://doi.org/10.1007/978-3-031-10766-5_27
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