Video Watermark Encoding Using 3D Temporal-Spatial Embedding
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Solution Overview
Problem
Existing video watermarking technologies often result in perceptible watermarks that negatively affect user experience and are easily detectable, making them ineffective against piracy.
Innovation Solution
Employing machine-learned models for imperceptible message watermarking by processing video data and message vectors through transformation and embedding portions to generate spatial-temporal encoding data, using discriminator and decoding models to refine the watermarking process, and adjusting model parameters based on loss functions to minimize perceptibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional watermarking methods are applied to video data, then watermarking capability is achieved, but the watermark becomes perceptible and affects user experience
Solution Approach 1:
The patent applies parameter changes by transforming the watermark embedding from traditional spatial-domain modifications to temporal-domain modifications. The system embeds watermarks as temporal patterns across video frames rather than spatial overlays, changing the fundamental parameter of where and how the watermark is placed. This temporal embedding approach makes the watermark imperceptible to human eyes while maintaining detection capability, directly resolving the contradiction between watermarking reliability and perceptibility.
Solution Approach 2:
The patent replaces traditional mechanical watermarking approaches (which involve direct spatial modification of video pixels) with a machine learning-based system that uses neural networks to generate and embed watermarks. The system substitutes the mechanical process of pixel manipulation with an intelligent system that learns optimal embedding patterns, achieving imperceptible watermarks through computational intelligence rather than direct spatial modification.
2Reliability
If traditional watermarking methods are used, then watermarking is applied to video data, but the watermark becomes easily detectable and obfuscatable
Solution Approach 1:
The patent introduces machine learning models as intermediary systems between the watermark embedding and detection processes. The neural network models serve as intermediaries that learn complex patterns and transformations, enabling the system to embed watermarks in a way that is difficult for traditional detectors to find. The intermediary ML models process the video data and generate watermarks that are embedded at a fundamental level, making them resistant to traditional detection and obfuscation methods.
Solution Approach 2:
The patent transitions from two-dimensional spatial watermark embedding to three-dimensional temporal-spatial embedding. By distributing the watermark information across multiple video frames in the temporal dimension, the system creates a watermark that is not visible in any single frame but can be detected through temporal analysis. This dimensional transformation makes the watermark much more difficult to detect and obfuscate compared to traditional spatial-only approaches.
3Object-affected harmful factors
If machine-learned models are used for imperceptible watermarking, then watermark perceptibility is reduced, but model complexity increases
Solution Approach 1:
The patent segments the complex watermarking system into distinct functional components: a watermark encoding model that generates temporal patterns, a watermark embedding model that integrates patterns into video data, and a watermark detection model that extracts embedded information. This segmentation allows each component to be optimized independently and simplifies the overall system architecture and training process, making the complex ML-based approach more manageable and practical.
Solution Approach 2:
The patent creates a universal watermarking system using machine learning models that can perform multiple functions: embedding watermarks imperceptibly, detecting watermarks robustly, and adapting to different video content and conditions. The neural network architecture is designed to be multi-functional, handling various watermark types and video characteristics through a single unified system, which reduces overall complexity compared to having separate specialized systems for each function.
Data Source
AI summary
Systems and methods of the present disclosure are directed to a computing system. The computing system can obtain a message vector and video data comprising a plurality of video frames. The computing system can process the input video with a transformation portion of a machine-learned watermark encoding model to obtain a three-dimensional feature encoding of the input video. The computing system can process the three-dimensional feature encoding of the input video and the message vector with an embedding portion of the machine-learned watermark encoding model to obtain spatial-temporal watermark encoding data descriptive of the message vector. The computing system can generate encoded video data comprising a plurality of encoded video frames, wherein at least one of the plurality of encoded video frames includes the spatial-temporal watermark encoding data.