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Methodological Approach for Identifying Websites with Infringing Content via Text Transformers and Dense Neural Networks

Author

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  • Aldo Hernandez-Suarez

    (Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico)

  • Gabriel Sanchez-Perez

    (Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico)

  • Linda Karina Toscano-Medina

    (Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico)

  • Hector Manuel Perez-Meana

    (Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico)

  • Jose Portillo-Portillo

    (Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico)

  • Jesus Olivares-Mercado

    (Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico)

Abstract

The rapid evolution of the Internet of Everything (IoE) has significantly enhanced global connectivity and multimedia content sharing, simultaneously escalating the unauthorized distribution of multimedia content, posing risks to intellectual property rights. In 2022 alone, about 130 billion accesses to potentially non-compliant websites were recorded, underscoring the challenges for industries reliant on copyright-protected assets. Amidst prevailing uncertainties and the need for technical and AI-integrated solutions, this study introduces two pivotal contributions. First, it establishes a novel taxonomy aimed at safeguarding and identifying IoE-based content infringements. Second, it proposes an innovative architecture combining IoE components with automated sensors to compile a dataset reflective of potential copyright breaches. This dataset is analyzed using a Bidirectional Encoder Representations from Transformers-based advanced Natural Language Processing (NLP) algorithm, further fine-tuned by a dense neural network (DNN), achieving a remarkable 98.71% accuracy in pinpointing websites that violate copyright.

Suggested Citation

  • Aldo Hernandez-Suarez & Gabriel Sanchez-Perez & Linda Karina Toscano-Medina & Hector Manuel Perez-Meana & Jose Portillo-Portillo & Jesus Olivares-Mercado, 2023. "Methodological Approach for Identifying Websites with Infringing Content via Text Transformers and Dense Neural Networks," Future Internet, MDPI, vol. 15(12), pages 1-31, December.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:12:p:397-:d:1297089
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    References listed on IDEAS

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