An Adaptive Information Collection Method for Collect Evidence of OTT Content Illegal Distribution Platform

This paper proposes an end credits-based method for recognizing illegal streaming videos to address the escalating issue of unauthorized distribution of digital video content in line with the expansion of OTT platforms.Traditional methods for identifying illegal content rely on comparing the overall features of videos, which pose limitations in recognition speed when dealing with large-scale content or real-time streaming environments.This study introduces a method that extracts copyright information from end credits to determine the distribution status of illegal content, demonstrating that analyzing only about 10% of the frames can effectively identify unauthorized content.The proposed method is expected to contribute to the protection of OTT content copyrights by reducing system load and efficiently detecting illegal digital video content even in large-scale environments.

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Publication Details

Journal
Journal of Software Assessment and Valuation
Published
2024-12-31
DOI
https://doi.org/10.29056/jsav.2024.12.23
Primary Topic
Artificial Intelligence Applications
Type
article
Field-Weighted Citation Impact
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article

An Adaptive Information Collection Method for Collect Evidence of OTT Content Illegal Distribution Platform

Byeong-Chan Park, W Lee, Injae Yoo, Seok-Yoon Kim et al.
Journal of Software Assessment and Valuation
Artificial Intelligence Applications
article

An Adaptive Information Collection Method for Collect Evidence of OTT Content Illegal Distribution Platform

Byeong-Chan Park, W Lee, Injae Yoo, Seok-Yoon Kim, Young‐Mo Kim, Se‐Young Jang, Seung Rim Shin
article en

Abstract

This paper proposes an end credits-based method for recognizing illegal streaming videos to address the escalating issue of unauthorized distribution of digital video content in line with the expansion of OTT platforms.Traditional methods for identifying illegal content rely on comparing the overall features of videos, which pose limitations in recognition speed when dealing with large-scale content or real-time streaming environments.This study introduces a method that extracts copyright information from end credits to determine the distribution status of illegal content, demonstrating that analyzing only about 10% of the frames can effectively identify unauthorized content.The proposed method is expected to contribute to the protection of OTT content copyrights by reducing system load and efficiently detecting illegal digital video content even in large-scale environments.

Journal of Software Assessment and ValuationVol. 20(4)
Openalex Percentile: Top 77%
Artificial Intelligence Applications
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