Neural Network Watermark Management in Digital Files
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Solution Overview
Problem
Existing methods for including information in digital files, such as watermarks, often result in improper or unnecessary information, obscuration of content, and missed critical metadata, leading to inefficiencies and user dissatisfaction.
Innovation Solution
The use of neural networks, specifically classification and generative neural networks, to detect, update, and manage watermarks within digital files, ensuring accurate and timely information inclusion while maintaining readability and compliance with access rights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If watermarks are included in digital files to provide attribution and access rights information, then information inclusion is improved, but content obscuration increases
Solution Approach 1:
The patent applies local quality by making watermarks semi-transparent rather than fully opaque, allowing different regions of the watermark to have varying levels of transparency. This enables the watermark to convey information while allowing the underlying content to remain partially visible, thus reducing obscuration while maintaining information inclusion.
Solution Approach 2:
The patent uses partial action by implementing watermarks that are not fully solid or opaque, but rather partially transparent. This partial application of the watermark allows it to provide necessary information while avoiding complete obscuration of the underlying content, striking a balance between information inclusion and content visibility.
2Reliability
If watermarks are manually added to digital files, then information inclusion can be controlled, but errors and omissions increase
Solution Approach 1:
The patent implements self-service by using machine learning models that automatically detect whether a watermark is present in a digital file and can automatically add watermarks when needed. The system autonomously makes decisions about watermark inclusion based on file analysis, eliminating manual intervention and reducing human errors while maintaining reliability.
Solution Approach 2:
The patent applies feedback mechanisms where the machine learning model continuously analyzes digital files, detects the presence or absence of watermarks, and provides feedback to automatically adjust or add watermarks as necessary. This feedback loop ensures high accuracy in watermark inclusion while automating the process to reduce complexity.
3Reliability
If intrusive watermarks are added to ensure information inclusion, then access rights are protected, but readability decreases
Solution Approach 1:
The patent applies local quality by implementing watermarks with varying transparency levels in different regions. The watermark provides sufficient coverage to protect access rights while maintaining areas of higher transparency that preserve document readability, allowing users to read content without excessive obstruction.
Solution Approach 2:
The patent uses parameter changes by adjusting the transparency, color, and positioning parameters of watermarks to optimize both protection and readability. By dynamically changing these parameters, the system can ensure access rights are protected while minimizing impact on document readability.
Data Source
AI summary
The described embodiments include an electronic device having a processor. The processor performs operations for handling watermarks in files. As part of the operations, the processor processes a portion of a file in a classification neural network to determine whether a watermark is present in the portion of the file. Based on a result of the processing, the processor performs an update associated with the watermark in the portion of the file. The processor then provides the updated portion of the file.