The copyright infringement issue and piracy concerns have increased due to the rapid spread of digital materials in the streaming services, social networks and the file sharing services. The manual and traditional digital rights management (DRM) systems are becoming less and less sufficient in their solution to the problem of the volume and variety of content distribution that nowadays exists. The current paper suggests an automated solution to protect digital content on the basis of the video analysis with the use of artificial intelligence (AI) and multilingual text recognition purposes. The system combines deep learning to analyze frames of videos with state of the art optical character recognition (OCR) and natural language processing (NLP) algorithms to identify copyrighted visual and textual content in a wide range of languages. The framework uses convolutional neural networks (CNNs) and transformer-based models to recognize scenes and objects, and multilingual OCR is used to guarantee the effective extraction of embedded text like subtitles, captions and watermarks. To quantify the accuracy of detection on various datasets, the metrics related to evaluation (such as precision, recall, and F1-score) are considered. The practical analysis reveals that the proposed solution surpass its baseline DRM tools with respect to the scalability, cross-lingual flexibility, and real-time tracking. The study adds to the understanding of affirming copyright protection measures, but also offers useful data to streaming providers, regulators and policy-makers. In addition, it underlines areas of future research, such as implementation of blockchain on unchangeable records of content ownership and development of multilingual support on under-represented languages.
Digital Content Protection, AI-Powered Video Analysis, Multilingual Text Recognition, Optical Character Recognition (OCR), Copyright Enforcement.
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