Barcode Authenticity Verification Using Gray Level Co-occurrence Matrix
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Solution Overview
Problem
Current methods for verifying the identity of printed items are limited by the need for deliberate overt or covert marks and cannot effectively distinguish genuine items from photocopies, especially on substrates like plastic films that lack unique features.
Innovation Solution
A method that examines and compares the artifacts of an unverified item with stored data from an original item, using statistical analysis and autocorrelation series to identify differences in magnitude ranges, thereby distinguishing photocopies from originals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If substrate variation methods are used for verification, then unique identification is achieved, but specialized perception systems are required and the method cannot be employed on substrates without readily identifiable unique features
Solution Approach 1:
The patent replaces specialized mechanical perception systems with standard imaging devices (cameras, scanners). Instead of requiring complex specialized equipment to detect substrate variations, the invention uses commonly available imaging devices to capture images that are then analyzed through image processing algorithms to extract verification features from printed marks and their surrounding areas.
Solution Approach 2:
The patent introduces an intermediary processing layer between the substrate and the verification system. Rather than directly detecting substrate variations with specialized equipment, the system uses standard imaging to capture the substrate, then employs image processing algorithms as an intermediary to extract and analyze verification features from the captured images, including both the printed mark and its surrounding area.
2Reliability
If overt or covert marks are added for verification, then item identity can be verified, but the item requires additional marks beyond those already present
Solution Approach 1:
The patent makes the printed mark serve multiple functions simultaneously. The same printed mark that provides commercial information (product identification, pricing, etc.) also contains embedded verification features that enable authenticity verification. The verification features are derived from characteristics of the printed mark itself and its surrounding area, eliminating the need for separate verification marks.
Solution Approach 2:
The printed mark performs self-verification through its own characteristics. The verification system analyzes features inherent in the printed mark (such as edge irregularities, dot patterns, and spatial relationships) that were created during the normal printing process. The mark essentially verifies itself by containing within its own structure the features needed for authentication, without requiring additional verification elements.
3Measurement precision
If high precision artifact analysis is performed to distinguish photocopies, then verification accuracy improves, but the complexity of the verification system increases
Solution Approach 1:
The patent divides the verification analysis into multiple independent segments or features. Instead of using a single complex analysis method, the system extracts and analyzes multiple distinct features separately (edge characteristics, dot patterns, spatial relationships, textural properties). Each feature provides a specific aspect of verification, and the combination of these segmented features achieves high accuracy while keeping each individual analysis component relatively simple.
Solution Approach 2:
The patent employs multiple different analysis parameters and metrics to evaluate various aspects of the printed mark. Instead of relying on a single complex parameter, the system changes and compares multiple parameters (such as edge sharpness, contrast ratios, spatial frequency distributions, and statistical properties of pixel intensities). This multi-parameter approach enables accurate photocopy detection while using standard, well-understood image processing techniques.
Data Source
AI summary
An anti-counterfeiting method involves dividing a barcode image into a plurality of modules; extracting a respective inertia of a gray level co-occurrence matrix for each of the plurality of modules; acquiring an image of a printed candidate bar code; generating a sorted list (using extracted intertias) for the plurality of modules of the image of the printed candidate barcode; in a first range of magnitudes, comparing the sorted list for the image of the printed genuine barcode with an equivalent sorted list for the image of the printed candidate barcode; and in a second range of magnitudes, comparing the sorted list for the image of the printed genuine barcode with the sorted list for the image of the printed candidate barcode, wherein the second range is different from the first range.