AI Image Analysis for Microscopic Modification Detection

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Solution Overview

Problem

Existing methods for analyzing object elements to detect authenticity or falsification only perform partial 'macroscopic' analysis, failing to detect microscopic modifications, and require expensive infrared microscopy equipment.

Innovation Solution

A method utilizing a learning artificial intelligence module to analyze selected image features of a digital image, including color, texture, gradation, glyph, inter-glyph distance, drawing line, and halftone dots, to determine both macroscopic and microscopic changes in an element, using convolutional neural networks and other neural networks for decision-making.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If macroscopic analysis method is used, then device complexity is reduced, but measurement precision deteriorates

Engineering Contradiction:

Improveanalysis device complexityVSAvoidmodification detection precision

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The analysis is segmented into multiple levels: macroscopic analysis of overall element structure and microscopic analysis of fine details like text, patterns, and surface features. This segmentation allows the system to maintain low device complexity while achieving high measurement precision by analyzing different scales separately

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transitions from single-scale macroscopic analysis to multi-scale analysis by introducing microscopic dimension. This dimensional expansion enables detection of both large-scale modifications and fine-detail alterations without requiring complex infrared microscopy equipment

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Measurement precision

If infrared microscopy equipment is used, then measurement precision is improved, but device complexity and cost increase

Engineering Contradiction:

Improvemicroscopic analysis capabilityVSAvoidequipment complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

Instead of using complex infrared microscopy equipment, the patent captures optical images and creates detailed digital copies that can be analyzed computationally. This copying approach achieves microscopic analysis capability while avoiding the complexity and cost of specialized infrared microscopy hardware

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces the mechanical/optical infrared microscopy system with a computational image analysis system. By substituting physical microscopy equipment with AI-based image processing, the system achieves comparable or superior measurement precision with reduced device complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If AI analysis of multiple image features is performed, then measurement precision is improved, but loss of time increases

Engineering Contradiction:

Improveauthenticity verification accuracyVSAvoidanalysis processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary analysis by first examining macroscopic features to quickly identify obvious modifications, then selectively analyzes microscopic features only when needed. This preliminary action reduces overall analysis time while maintaining high precision by avoiding unnecessary detailed examination of clearly authentic elements

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12548284B2Method for analyzing one or more element(s) of one or more photographed object(s) in order to detect one or more modification(s), and associated analysis device

Publication Date: 2026.02.10 CYPHEME

AI summary

A method analyzes an element associated with an object and representative of the latter. The method includes receiving first data defining a digital image of at least a part of the element resulting from a photographic capture, providing the first data to an analyzer including at least one learning artificial intelligence module, previously configured with second training data of at least one reference element, in order to make a decision relating to the first data received from a selection of at least a portion of these first data defining a chosen zone of interest and characterized by at least one chosen image feature, this decision being related to a potential modification of the analyzed element with respect to a corresponding reference element.