Relational Fingerprint for Image Authentication
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Solution Overview
Problem
Existing image recognition and authentication methods fail to account for intrinsic randomness in images, preventing effective authentication and identification of unique variability in attributes such as texture or contours.
Innovation Solution
A method for determining a relational fingerprint between two images using similarity vectors, calculated through a similarity indicator and entropic criterion, which captures the unique and unpredictable properties of physical subjects, allowing for authentication and identification by correlating images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image matching methods are used to determine similarity between images, then the degree of similarity can be determined, but authentication of the images or subjects cannot be performed
Solution Approach 1:
The patent segments the image into multiple tiles or blocks, and calculates similarity vectors for each tile independently. This segmentation allows the system to capture local variations and intrinsic randomness in different regions of the image, which is essential for authentication. By dividing the image into smaller units, the method can identify unique patterns that persist across different acquisitions of the same physical subject, enabling reliable authentication while maintaining similarity measurement capability.
2Productivity
If conventional image recognition methods are applied, then image processing can be performed, but intrinsic randomness and unique variability of physical subjects cannot be captured
Solution Approach 1:
The patent changes the parameters used for image analysis by calculating similarity vectors between corresponding tiles of two images and then analyzing the distribution of these vectors using entropic criteria. Instead of traditional feature extraction, the method transforms the image data into a similarity vector field and computes entropy-based metrics that specifically capture intrinsic randomness. This parameter transformation enables the preservation and utilization of unique variability information that conventional methods would discard.
3Measurement precision
If similarity vectors are calculated for all tiles, then comprehensive comparison is achieved, but computational complexity increases
Solution Approach 1:
The patent extracts only the essential information needed for authentication by calculating similarity vectors for tiles and then focusing analysis on specific regions of the similarity vector field where disordered patterns indicate intrinsic randomness. Rather than processing all similarity vectors equally, the method identifies and extracts key regions that contain authentication-critical information, reducing computational burden while maintaining comprehensive comparison capability.
Data Source
Figure 1-A~1-E
Figure 2-A~2-G
Figure 3-A~3-C
AI summary
The invention relates to a method for determining a relational imprint between two images comprising the following steps - the implementation of a first image and of a second image, - a phase of calculating vectors of similarity between tiles belonging respectively to the first and second images, the similarity vectors forming a field of imprint vectors, the field of imprint vectors comprising at least one haphazard region disordered in the sense of an entropy criterion, - a phase of recording in the guise of relational imprint of a representation of the calculated field of imprint vectors. The invention also relates to a method for authenticating a candidate image with respect to an authentic image implementing the method for determining a relational imprint according to the invention.