Video Watermarking: Safeguarding Your Video from (Unauthorized) Annotations by Video-based LLMs
This addresses data protection concerns for video content creators and owners against misuse by evolving video-based LLMs, representing a domain-specific incremental solution.
The paper tackles the problem of unauthorized video annotations by video-based LLMs by introducing Video Watermarking, a technique that embeds imperceptible watermarks into key frames to reduce video comprehensibility, with experiments showing significant effectiveness across various models.
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.