Digital Watermark Training With Distortion-Aware Decoding
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Solution Overview
Problem
Existing watermarking technologies struggle to achieve robustness, blindness, and automated embedding and extraction simultaneously, especially in the presence of distortions, and lack efficient detection mechanisms to reduce computational resources.
Innovation Solution
A joint training method for encoder and decoder machine learning models that generate and decode digital watermarks, incorporating distortion detection to minimize errors and optimize training parameters, allowing for visually imperceptible watermarks to be detected and decoded efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional manually designed watermarking methods are used, then they can handle specific types of distortions, but they cannot simultaneously handle a broad spectrum of distortions
Solution Approach 1:
The patent changes the fundamental parameters of watermarking from manual design to automated deep learning-based embedding. The encoder network learns optimal embedding patterns through training on distorted images, enabling the system to adapt to various distortion types automatically rather than requiring manual design for each distortion type
Solution Approach 2:
The patent replaces manual watermarking mechanisms with automated machine learning systems. Instead of manually designing watermarks that can withstand specific distortions, the system uses trained neural networks to automatically embed and extract watermarks robustly across multiple distortion types
2Extent of automation
If deep learning networks are incorporated with image watermarking, then automated embedding and extraction can be achieved, but existing systems cannot achieve robustness, blindness and automated embedding and extraction simultaneously
Solution Approach 1:
The patent implements a feedback mechanism where the decoder network receives distorted watermarked images and outputs decoded watermarks, which are then fed back to the encoder for re-embedding. This feedback loop enables the system to learn and adapt to distortion patterns, achieving both automation and robustness simultaneously
Solution Approach 2:
The patent creates a universal watermarking system where a single trained encoder-decoder pair can handle multiple distortion types and scenarios. The networks are trained to be blind to various distortions, making them multi-functional across different application contexts without requiring system redesign
3Reliability
If complex watermarking systems are used to ensure robustness, then watermark reliability improves, but computational resources and processing time increase
Solution Approach 1:
The patent optimizes computational parameters by training efficient neural network architectures with appropriate complexity. The networks are trained to achieve robustness with reasonable computational cost, balancing reliability and energy consumption through careful model design and training procedures
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly training an encoder that generates a watermark and a decoder that decodes a data item encoded within the watermark. The training comprises obtaining a plurality of training images and data items. For each training image, a first watermark is generated using an encoder and a subsequent second watermark is generated by tiling two or more first watermarks. The training image is watermarked using the second watermark to generate a first error value and distortions are added to the watermarked image. A distortion detector predicts the distortions based on which the distorted image is modified. The modified image is decoded by the decoder to generate a predicted data item and a second error value. The training parameters of the encoder and decoder are adjusted based on the first and the second error value.