Securing Digital Media Assets: Advanced Machine Learning Approaches for IP Protection
DOI:
https://doi.org/10.32628/CSEIT241061230Keywords:
Machine Learning, Intellectual Property Protection, Digital Rights Management, Content Security, Neural ArchitecturesAbstract
This article explores the transformative role of machine learning in protecting intellectual property within the digital media and content creation landscape. The article examines advanced approaches to securing digital assets through neural architectures, object detection models, and audio-visual analysis systems. It investigates the implementation of cloud-based protection pipelines, distributed monitoring architectures, and real-time processing frameworks that enhance content security. The article delves into industry applications across social media monitoring, streaming services, and digital publishing platforms, highlighting the effectiveness of automated protection mechanisms. Furthermore, it addresses implementation challenges and solutions, focusing on large-scale processing strategies, accuracy optimization, and cross-border protection issues. The article also discusses integrating blockchain technology with Digital Rights Management systems and examines emerging trends in multi-accelerator architectures for content protection. This article provides insights into best practices and future directions for securing intellectual property in the evolving digital media ecosystem through a comprehensive article analysis of various case studies and industry implementations.
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