arXiv:2606.14786v1 Announce Type: cross Abstract: Content moderation is critical for online video platforms to ensure content safety, protect creators, and sustain positive user experiences. Beyond filtering harmful content, platforms must guarantee content authenticity at scale so that users are exposed to diverse, original videos rather than low-value reproductions. We present MatchLM2Lite, a real-time, production-grade reproduced content identification (RCI) system that leverages the powerful understanding of a multimodal large language model (MLLM) distilled into a small and fast-inference
The proliferation of generative AI tools necessitates advanced content moderation techniques to combat reproduced and synthetic media at scale.
This development addresses the critical need for online platforms to maintain content authenticity and user trust amidst an increasing volume of easily reproducible digital content.
The ability to identify reproduced content in real-time using efficient MLLM-based systems improves content moderation efficacy and platform integrity.
- · Online video platforms
- · Content creators focused on original content
- · AI-powered content moderation solution providers
- · Users seeking authentic digital experiences
- · Platforms with weak content authenticity controls
- · Creators of low-value reproduced content
- · Pirates and content infringers
Online video platforms can more effectively filter duplicated or low-quality content, improving content quality for users.
Enhanced content authenticity fosters greater trust in platforms and potentially increases engagement with original creators.
The widespread adoption of such systems could influence economic models around content creation and distribution, making unique content more valuable and harder to 'game'.
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Read at arXiv cs.AI