Steganographic Imaging for Counterfeit Product Detection
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Solution Overview
Problem
Current methods for detecting counterfeit products are costly, complex, and lack effective utilization of distributed mobile computing resources, particularly in accurately identifying and cataloging counterfeit items at scale without significant capital and time expenditures.
Innovation Solution
AI-based steganographic systems that leverage smartphone infrastructure for digital imaging and machine learning to analyze pixel data, utilizing crowdsourced data to distinguish between authentic and counterfeit products by training models with synthesized and real-world images, and maintaining a counterfeit list for rapid authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex identifying marks (e.g., data matrix codes, QR codes) are added to products for counterfeit detection, then counterfeit detection capability is improved, but capital expenses for replacing and/or retrofitting existing equipment become prohibitive
Solution Approach 1:
The patent uses existing product codes and printing infrastructure to create counterfeit detection capabilities without requiring new specialized equipment. The system copies and analyzes existing printed codes (barcodes, alphanumerics) that are already part of the manufacturing process, rather than requiring implementation of complex new identifying marks like data matrix codes or QR codes.
Solution Approach 2:
The system makes existing printing equipment multi-functional by enabling it to produce codes that serve both their original purpose (product identification, tracking) and an additional counterfeit detection function. The same printer that produces standard barcodes also produces steganographically modified codes that can be analyzed for authenticity, eliminating the need for dedicated counterfeit protection equipment.
2Measurement precision
If image-based analysis systems are used to detect counterfeit items, then detection accuracy is improved, but the vast numbers and variety of counterfeit images create difficulties in building robust systems due to the need to gather and access various counterfeit images
Solution Approach 1:
Instead of gathering vast numbers of counterfeit images to train detection systems, the patent inverts the approach by using synthesized authentic images with known properties to create training data. The system generates synthetic counterfeit images by applying transformations to authentic images, eliminating the need to physically collect and catalog real counterfeit examples from the marketplace.
Solution Approach 2:
The system uses the authentic product images and printing processes themselves to generate the training data needed for detection. By synthesizing counterfeit images from authentic sources and using the existing product code structures, the system serves its own training data needs without external collection efforts, automatically creating the dataset required for robust detection.
3Reliability
If real-world images of counterfeit products are used to train machine learning models, then model robustness is improved, but it is prohibitively costly or time consuming to obtain, organize, structure, or aggregate such vast numbers of images
Solution Approach 1:
The patent performs preliminary synthesis of training images before the machine learning training process begins. By pre-generating synthetic counterfeit images from authentic sources with known characteristics, the system prepares the complete training dataset in advance, eliminating the time-consuming activities of obtaining, organizing, and structuring real-world counterfeit images during or before the training phase.
Solution Approach 2:
The system creates synthetic copies of authentic product images with embedded counterfeit characteristics, using these synthesized images as training data. This copying approach generates unlimited training examples without requiring physical collection of real counterfeit products, dramatically reducing the time and resources needed to assemble training datasets while maintaining model robustness.
Data Source
AI summary
A counterfeit and imaging detection system includes a processor, a counterfeit product detection app, and a steganographic imaging model, electronically accessible by the counterfeit product detection app trained using image data and configured cause the processor to obtain a digital image of a physical product of a product line, the digital image captured by an imaging device and the digital image comprising pixel data, analyze the digital image to detect within the pixel data a batch code uniquely identifying a batch of the physical product of the product line, analyze the pixel data of the digital image to determine that the batch code is counterfeit, and augment a counterfeit list of batch codes to include the batch code, wherein the counterfeit list of batch codes remains electronically accessible to the counterfeit product detection app for one or more further counterfeit detection iterations.