Authors :
Niharika Patidar; Dr. Sachin Patel
Volume/Issue :
Volume 10 - 2025, Issue 12 - December
Google Scholar :
https://tinyurl.com/2yc2hvjd
Scribd :
https://tinyurl.com/34c4r9b5
DOI :
https://doi.org/10.38124/ijisrt/25dec1148
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Today video content now defines over 82% of global internet traffic, the explosion of user-generated content (UGC)
has fundamentally overwhelmed our ability to keep platforms safe. Traditional moderation systems were simply not
designed for this unprecedented volume of content. They are too slow, too rigid, and completely blind to the cultural context
that defines modern toxicity. This is where the true innovation lies: CCHE (Content Classification and Harm Evaluation) is
an open-source pipeline for downloading short-form video from public sources, sampling representative frames, and
inferring content typology and age suitability using a large vision-language model. The shift towards Large Vision-Language
Models (LVLMs) is not an option; it is an urgent necessity. This paper furnishes a comprehensive technical examination of
this transition, contrasting proprietary titans like GPT-4o with the thrilling advancements in open-source alternatives, and
critically dissecting the engineering frameworks from ingestion efficiency to industrial deployment required for robust, real-
world content safety.
References :
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Today video content now defines over 82% of global internet traffic, the explosion of user-generated content (UGC)
has fundamentally overwhelmed our ability to keep platforms safe. Traditional moderation systems were simply not
designed for this unprecedented volume of content. They are too slow, too rigid, and completely blind to the cultural context
that defines modern toxicity. This is where the true innovation lies: CCHE (Content Classification and Harm Evaluation) is
an open-source pipeline for downloading short-form video from public sources, sampling representative frames, and
inferring content typology and age suitability using a large vision-language model. The shift towards Large Vision-Language
Models (LVLMs) is not an option; it is an urgent necessity. This paper furnishes a comprehensive technical examination of
this transition, contrasting proprietary titans like GPT-4o with the thrilling advancements in open-source alternatives, and
critically dissecting the engineering frameworks from ingestion efficiency to industrial deployment required for robust, real-
world content safety.
