Video Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMs
arxiv(2024)
Abstract
The advent of video-based Large Language Models (LLMs) has significantly
enhanced video understanding. However, it has also raised some safety concerns
regarding data protection, as videos can be more easily annotated, even without
authorization. This paper introduces Video Watermarking, a novel technique to
protect videos from unauthorized annotations by such video-based LLMs,
especially concerning the video content and description, in response to
specific queries. By imperceptibly embedding watermarks into key video frames
with multi-modal flow-based losses, our method preserves the viewing experience
while preventing misuse by video-based LLMs. Extensive experiments show that
Video Watermarking significantly reduces the comprehensibility of videos with
various video-based LLMs, demonstrating both stealth and robustness. In
essence, our method provides a solution for securing video content, ensuring
its integrity and confidentiality in the face of evolving video-based LLMs
technologies.
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