Machine-Learning Watermark Detection for Imperceptible Image Noise
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Solution Overview
Problem
Existing watermarking techniques struggle to balance visibility and security, with decoders potentially identifying and copying embedded codes, necessitating further improvements to enhance security.
Innovation Solution
Embedding a watermark as a noise component imperceptible to the human eye and using a machine-learning based scheme to identify it, trained on various noise patterns, allowing for secure and reliable verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the identification code is made imperceptible to the human observer, then the security against unauthorized copying is improved, but the difficulty of identifying and verifying the code increases
Solution Approach 1:
The patent introduces a machine learning-based decoder as an intermediary that can perceive and interpret the imperceptible watermark. The decoder has been trained to recognize the specific noise patterns containing the identification code, allowing reliable verification without human observers needing to see the watermark directly.
Solution Approach 2:
The watermark is embedded as noise with specific statistical parameters (amplitude, frequency distribution, spatial distribution) that make it imperceptible to humans. The machine learning decoder is trained to recognize these specific parameter patterns, creating a detection mechanism that operates in the parameter space rather than the visual space.
2Ease of operation
If the watermark amplitude is raised to be visible to human viewers, then the ease of identifying the code is improved, but the security against copying deteriorates
Solution Approach 1:
The machine learning decoder serves as an intermediary that bridges the gap between the imperceptible watermark and human verification needs. It performs the identification function that would otherwise require human visual detection, allowing the watermark to remain invisible while maintaining verification capability.
Solution Approach 2:
The patent replaces the human visual-mechanical detection system with an automated machine learning-based detection system. This substitution allows the watermark to operate at amplitudes below human perception thresholds while maintaining robust detection through algorithmic analysis of noise patterns.
3Reliability
If the watermark is embedded as noise imperceptible to humans, then the security is enhanced, but the ability of traditional decoders to identify the information is lost
Solution Approach 1:
The patent changes the parameter space in which the watermark operates - using specific noise characteristics (statistical distributions, frequency spectra, spatial correlations) that are imperceptible to humans but recognizable by trained machine learning models. This parameter transformation preserves information while enhancing security.
Solution Approach 2:
The machine learning decoder acts as a specialized intermediary trained to translate the noise-encoded information into readable form. It has learned the specific parameter patterns used during embedding, allowing it to extract information that traditional decoders cannot detect in the noise domain.
Data Source
AI summary
The present disclosure generally relates to a method adapted to identifying a predefined component embedded within image data of a target object. This is in line with the present disclosure achieved by applying a machine-learning based scheme that has been arranged to identify a noise component from an image illustrating the target object. The present disclosure also relates to a corresponding computer system and a computer program product.