Watermark Extraction Across Zoom Levels With ML Screening
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Solution Overview
Problem
Existing technologies face challenges in efficiently detecting and decoding visually imperceptible watermarks in images, particularly when captured at varying zoom levels and with distortions, leading to excessive computational resources and time consumption.
Innovation Solution
A machine learning-based approach is employed to detect and decode watermarks using trained models that are agnostic to zoom levels and image distortions, involving a detector model for initial screening and a decoder model for accurate watermark extraction, reducing computational overhead by filtering out images without watermarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional watermark detection methods are used to ensure accurate detection and decoding, then measurement precision is improved, but computational resources and processing time increase significantly
Solution Approach 1:
The patent divides the watermark detection process into two distinct stages: a fast screening phase using a lightweight detector model to quickly identify candidate images containing watermarks, and a detailed decoding phase using a more sophisticated decoder model only on the screened candidates. This segmentation allows the system to maintain high detection accuracy while significantly improving processing throughput by avoiding exhaustive analysis of all images.
Solution Approach 2:
The detector model performs preliminary screening of images to identify those likely containing watermarks before the more computationally intensive decoder model is applied. This preliminary action filters out the majority of images that do not contain watermarks, reducing the computational burden on subsequent processing stages while maintaining accurate detection of actual watermarked images.
2Reliability
If comprehensive watermark detection is performed on all images to ensure reliability, then detection reliability is improved, but processing time increases
Solution Approach 1:
The detection system is segmented into a rapid filtering stage using the detector model and a reliable decoding stage using the decoder model. This segmentation ensures that only images passing the initial reliability check undergo comprehensive analysis, thereby maintaining high detection reliability while minimizing overall processing time.
Solution Approach 2:
The system applies partial detection action (screening) to all images through the lightweight detector model, and excessive/detailed action (full decoding) only to the small subset of images that pass the screening. This approach ensures reliable detection of watermarked images while avoiding unnecessary processing of non-watermarked images.
3Adaptability or versatility
If a single detection model is used to handle all zoom levels and distortions, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the adaptability requirement into two components: the detector model handles basic zoom-level-independent screening with simple architecture, while the decoder model handles detailed decoding of distorted watermarks. This segmentation allows each model to be optimized for its specific function, maintaining adaptability across zoom levels without excessive complexity in either model.
Solution Approach 2:
The detector model is designed as a universal screening tool that operates effectively across different zoom levels and image conditions, while the decoder model provides specialized decoding capability. Together, they form a multi-functional system that achieves broad adaptability without requiring a single overly complex model to handle all scenarios.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting and decoding a visually imperceptible or perceptible watermark. A watermark detection apparatus determines whether the particular image includes a visually imperceptible or perceptible watermark using detector a machine learning model. If the watermark detection apparatus detects a watermark, the particular image is routed to a watermark decoder. If the watermark detection apparatus cannot detect a watermark in the particular image, the particular image is filtered from further processing. The watermark decoder decodes the visually imperceptible or perceptible watermark detected in the particular image. After decoding, an item depicted in the particular image is validated based data extracted from the decoded visually imperceptible or perceptible watermark.