Composite Forgeries Detection via Feature Position Verification
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Solution Overview
Problem
Existing methods fail to effectively detect composite counterfeit documents of value, which are composed of parts from genuine and counterfeit documents, as they often produce similar measurement signals to genuine documents, making it difficult to distinguish between real and forged items.
Innovation Solution
The method involves determining the positions of specific features on a document, comparing these positions to expected values based on standard data, and evaluating the differences to determine if they fall within acceptable thresholds, allowing for the detection of composite forgeries by identifying deviations that indicate assembly or alteration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fluorescence properties are used to detect counterfeit banknotes, then genuine banknote paper can be distinguished from ordinary paper, but composite counterfeits with similar fluorescence signals cannot be detected
Solution Approach 1:
The patent segments the banknote into multiple regions with features at different positions (first feature at position P1, second feature at position P2). By measuring and comparing the distances between these segmented features with standard values, the system can detect composite forgeries that have similar overall fluorescence signals but incorrect internal spatial relationships.
2Reliability
If adhesive strip detection methods are used to detect composite forgeries, then assembled forgeries can be identified, but counterfeits without adhesive strips or with similar measurement signals cannot be detected
Solution Approach 1:
The patent employs a universal detection method that measures distances between multiple features at different positions throughout the banknote. This multi-functional approach can detect various types of forgeries including adhesive strips, composite forgeries without strips, and counterfeits with similar fluorescence signals, making the system adaptable to diverse counterfeit methods.
3Reliability
If multiple features at different positions are measured and compared, then composite forgeries can be detected through position verification, but the detection system becomes more complex
Solution Approach 1:
The patent replaces complex mechanical or manual inspection systems with an automated optical measurement system that uses sensors to detect feature positions and calculate distances. This substitution simplifies the overall system while maintaining high detection capability, as the automated system can rapidly measure multiple positions and compare them against stored standard values without manual intervention.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the accurate identification of composite forgeries and genuine documents that have been repaired or tampered with, improving the detection of counterfeit documents by analyzing position and structural differences beyond fluorescence properties.
Implementation Method 1
the banknotes are also checked, for example, for properties that distinguish genuine banknote paper from ordinary paper, such as its fluorescence properties
Data Source
Figure 1a~1b
Figure 2a~2b
AI summary
The invention relates to a method and a device for identifying forged value documents, for example composed forgeries consisting of parts of different value documents. According to the method, a characteristic of a first feature of the value document to be verified is determined in a first step and a characteristic of a second feature of the value document to be verified is determined in a second step. In an additional step, the characteristics of both features are compared and the difference is determined. Alternatively, in the additional step an anticipated characteristic for the second feature is compared to the characteristic of the second feature and the difference is determined. Verification then takes place as to whether the determined difference lies in a predetermined value range that contains target values which are defined as acceptable for said difference.