Watermark Decoding Across Zoom Levels With ML Pre-Detection
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Solution Overview
Problem
Existing methods for detecting and decoding visually imperceptible watermarks in images are inefficient and computationally expensive, requiring analysis at multiple zoom levels and failing to account for image distortions, leading to wastage of resources and time.
Innovation Solution
A zoom agnostic approach using machine learning models trained to detect and decode watermarks across varying zoom levels and image distortions, incorporating numerical rounding to improve model performance, and employing a detection process to filter out images without watermarks before decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods analyze images at multiple zoom levels to detect watermarks, then detection coverage is improved, but computational resources and time are excessively consumed
Solution Approach 1:
The patent changes the parameter of zoom level analysis from multiple discrete levels to a continuous zoom-agnostic approach. The machine learning model is trained to process images at any zoom level without requiring separate analysis at each level, thereby maintaining detection coverage while eliminating redundant computational resources.
Solution Approach 2:
The patent creates a universal watermark detection model that functions across all zoom levels simultaneously. The single model handles both detection and decoding tasks regardless of the input image's zoom level, replacing the need for multiple specialized analysis processes and improving overall system efficiency.
2Reliability
If existing methods process all images through decoding processes, then comprehensive watermark recovery is achieved, but unnecessary computational resources are wasted on images without watermarks
Solution Approach 1:
The patent applies preliminary detection action before the decoding process. The machine learning model first detects whether a watermark is present in the image, and only if detection is successful does the system proceed to decoding. This preliminary filtering prevents unnecessary decoding operations on images without watermarks, significantly reducing computational waste.
Solution Approach 2:
The patent extracts the detection function as a separate preliminary step from the decoding process. By taking out the detection operation and placing it before decoding, the system can identify and filter out images without watermarks, ensuring that only relevant images undergo the computationally expensive decoding process.
3Measurement precision
If existing methods use complex multi-level zoom analysis, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges the detection and decoding functions into a single integrated machine learning model. This unified model processes images at any zoom level without requiring separate analysis pipelines for different zoom levels, thereby reducing system complexity while maintaining detection accuracy through the model's zoom-agnostic training.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a visually imperceptible or a visually perceptible watermark and outputting a result based on the determination. A watermark decoder receives an input image. The watermark decoder applies a decoder machine learning model to decode a watermarks at different levels of zoom. The water mark decoder determines whether a watermark was decoded to obtain a decoded watermark. The watermark decoder outputs a result based on the determination whether the watermark was decoded through application of the decoder machine learning model to the input image that includes outputting a zoomed output decoded through application of the decoder machine learning model to the input image.