Line Art Signal Encoding for Printed Document Authentication
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Solution Overview
Problem
Existing digital watermarking technologies face challenges in ensuring robustness and perceptual quality, particularly in applications involving printed objects with limited image variability, such as uniform colors, where embedding data channels is difficult due to minimal host content.
Innovation Solution
A signal encoder and decoder system utilizing trained neural networks and perceptual models to optimize the embedding and detection of digital watermarks in host images, adjusting host signal values based on perceptual and robustness models to ensure visibility and resistance to distortions, with protocols selected based on application-specific parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If digital watermarking is applied to printed objects with uniform colors and limited image variability, then data encoding capability is improved, but embedding reliability deteriorates due to minimal host content
Solution Approach 1:
The system changes the parameter space for embedding from relying on image content variability to using perceptual feature spaces and neural network representations. By transforming the embedding problem into a perceptual feature domain, the system can reliably embed data even in uniform color regions where traditional spatial or frequency domain methods fail.
Solution Approach 2:
The patent replaces traditional mechanical/image-based embedding mechanisms with neural network-based perceptual embedding. Instead of relying on spatial frequencies or color variations in the image, the system uses trained neural networks to extract and embed data in a perceptual feature space, enabling reliable encoding in uniform regions.
2Ease of manufacture
If traditional digital watermarking methods are used in printed objects, then ease of implementation is improved, but detection accuracy deteriorates due to limited image variability
Solution Approach 1:
The system substitutes traditional image processing-based detection with neural network-based perceptual detection. The trained neural networks analyze perceptual features rather than relying on spatial or frequency patterns, enabling accurate detection even when the host image has uniform colors and limited variability.
Solution Approach 2:
The system employs feedback mechanisms where the neural networks are trained on labeled data to recognize and detect watermarks. The training process incorporates feedback from detection results, allowing the models to improve their accuracy in detecting watermarks in uniform regions through iterative learning and refinement.
3Adaptability or versatility
If data is embedded in uniform color regions, then adaptability to different printed objects is improved, but signal robustness deteriorates against common distortions
Solution Approach 1:
The system changes the embedding parameter space from spatial or frequency domains to perceptual feature domains using neural networks. This transformation allows the embedded signals to be represented in terms of perceptual characteristics that are more resistant to common printing distortions, while still adapting to different object types and uniform color regions.
Data Source
AI summary
The present disclosure relates generally to signal encoding for value documents. One aspect of the technology relates to authentication of value documents comprising line art patterns. In one example, authentication relies of detection of signals encoded within the line art, and expected frequency domain analysis of the line art. Other combinations are described as well. For example, authentication may rely on detection of signals encoded within encoded and then printed documents, expected frequency domain characteristics encoded signals, presence or absence of halftoning, a color check and/or trained classifier analysis. Other aspects, technology, claims and combinations are described as well.