Zoom-Agnostic Watermark Decoding with Detector-First Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting and decoding visually imperceptible watermarks in images are computationally expensive and inefficient, particularly when images are captured at varying zoom levels or with distortions, leading to wastage of resources and time.
Innovation Solution
A zoom-agnostic machine learning model is employed to detect and decode watermarks, utilizing a detection process before decoding to filter out images without watermarks, and applying numerical rounding techniques to improve model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional watermark decoding methods are applied to all input images, then watermark detection completeness is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent applies a detector machine learning model to perform preliminary detection of watermarks before attempting full decoding. This preliminary action filters out images without watermarks, so that the computationally expensive decoder model is only applied to images that actually contain watermarks, thereby maintaining detection completeness while significantly reducing overall computational resource consumption.
2Measurement precision
If decoder model is applied to images at multiple zoom levels, then watermark decoding accuracy is improved, but processing time increases
Solution Approach 1:
The patent trains the decoder machine learning model to be zoom-agnostic by using training images at multiple zoom levels (e.g., 0.5x, 1x, 1.5x, 2x) during the training phase. This allows the model to maintain high decoding accuracy across different zoom levels without requiring separate processing for each zoom level, thereby reducing processing time while preserving accuracy.
3Reliability
If comprehensive watermark detection is performed on all images, then detection reliability is improved, but processing efficiency decreases
Solution Approach 1:
The patent segments the watermark processing task into two distinct stages: (1) a lightweight detection stage using a detector model that quickly identifies images containing watermarks, and (2) a detailed decoding stage using a more sophisticated decoder model applied only to detected watermarked images. This segmentation maintains high detection reliability while significantly improving overall processing efficiency by avoiding unnecessary decoding operations on non-watermarked images.
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.