AI Steganographic Pixel Analysis for Product Counterfeit Detection
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Solution Overview
Problem
Existing methods for detecting product counterfeiting rely on vast numbers of real-world images of non-authentic products, which are costly, time-consuming, and prone to errors due to underrepresented depictions, making it difficult to build robust machine learning models.
Innovation Solution
AI-based steganographic systems train models using synthesized images of authentic and counterfeit products, allowing for rapid development of accurate detection systems without the need for extensive real-world image collections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vast numbers of real-world images of non-authentic products are used to train machine learning models, then the detection system can be built, but it becomes costly, time-consuming, and prone to errors due to underrepresented depictions
Solution Approach 1:
The patent uses synthesized images generated through image processing techniques to replicate counterfeit product appearances without requiring actual counterfeit images. This copying approach allows the training dataset to include diverse counterfeit representations while avoiding the time-consuming process of collecting and verifying real-world counterfeit images.
Solution Approach 2:
The system performs preliminary image processing and synthesis to create training images before the actual model training begins. By pre-generating synthesized counterfeit images with known characteristics, the system eliminates the need to search for and verify real counterfeit images during the training process, significantly reducing preparation time.
2Measurement precision
If real-world counterfeit images are collected and organized for training, then the model can learn from actual data, but it becomes prohibitively costly and time consuming to obtain, organize, structure, or otherwise aggregate
Solution Approach 1:
Instead of collecting and organizing real counterfeit images, the system creates synthetic copies of product images with artificially introduced counterfeit features. This approach maintains measurement precision by ensuring known ground truth in synthesized images while dramatically improving ease of manufacture by automating the image generation process.
Solution Approach 2:
The system uses automated image processing algorithms to generate synthesized counterfeit images without requiring manual collection, organization, or annotation of real counterfeit images. The self-service approach allows the system to create its own training data with consistent quality and known characteristics.
3Reliability
If manual processing and manipulation are performed to prepare training datasets, then the data quality can be ensured, but it causes significant delays and errors in preparing or supervising the training dataset
Solution Approach 1:
The patent replaces manual mechanical processing and manipulation of images with automated computer-based image processing algorithms. This substitution maintains training data quality through consistent automated application of image processing techniques while dramatically increasing productivity by eliminating manual labor bottlenecks.
Solution Approach 2:
The system automatically adjusts image processing parameters such as noise levels, distortion types, and feature modification intensities to ensure training data quality. By programmatically controlling these parameters, the system maintains reliability while achieving high-speed automated dataset preparation.
Data Source
AI summary
AI based steganographic systems and methods are described for analyzing pixel data of a product to detect product counterfeiting. An AI based imaging model is trained with pixel data of a plurality of training images comprising (1) a first subset of images each depicting at least a portion of the product having one or more authentic steganographic features, and (2) a second subset of images each depicting at least a portion of the product devoid of the one or more authentic steganographic features. A new image comprising pixel data of the product may be received and analyzed by the AI based imaging model to detect the pixel-based feature presence or absence of authentic steganographic feature(s) to determine an image classification of the product. The AI based steganographic systems and methods may detect whether the product is authentic or counterfeit based on the image classification.