Semi-supervised Clustering for Counterfeit Detection
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Solution Overview
Problem
Current methods face challenges in identifying counterfeit products due to limited counterfeit data for training detection models and the difficulty in distinguishing authentic and counterfeit items, as counterfeits are designed to be identical to the original products, making it hard to create a complete dataset for training identification models.
Innovation Solution
A computer program product utilizing a siamese neural network with shared parameters to generate image embeddings, combined with K-medoids clustering, determines the integrity of clusters based on ground truth labels and total data points, and selects the best K-medoids model to create authentic and counterfeit clusters, generating new clusters for outlier embeddings to identify counterfeit resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional detection models are trained with limited counterfeit data, then the model can identify known counterfeit patterns, but it fails to detect previously unknown counterfeit patterns
Solution Approach 1:
The system performs preliminary clustering of image embeddings into multiple groups before final authentication. By pre-organizing data into clusters with representative medoids and thresholds, the system establishes a framework that can accommodate both known and unknown counterfeit patterns without requiring complete training data coverage.
Solution Approach 2:
The system creates synthetic training data by copying and transforming existing image embeddings into the embedding space. This allows the model to learn from replicated patterns that simulate unknown counterfeit variations, enabling detection of previously unseen counterfeit types while maintaining training efficiency.
2Measurement precision
If the system creates multiple clusters for authentication, then it can distinguish between authentic and counterfeit products, but it requires defining appropriate cluster thresholds and medoids
Solution Approach 1:
The system automatically selects cluster medoids from the data itself rather than requiring manual definition. The medoids are chosen as representative elements from the embedding space that naturally emerge from the clustering process, reducing the need for complex threshold configuration while maintaining accurate authentication.
Solution Approach 2:
The system dynamically adjusts cluster thresholds and medoid selections based on the distribution of image embeddings in the embedding space. By adapting these parameters to the actual data characteristics, the system achieves high authentication accuracy without requiring fixed, manually-defined thresholds for each cluster.
3Measurement precision
If the system uses deep learning models for counterfeit detection, then it can extract meaningful features from images, but it requires large amounts of training data which are not available for counterfeit products
Solution Approach 1:
The system generates synthetic training examples by copying and transforming existing image embeddings into the embedding space. This creates virtual training data that preserves the essential features of counterfeit patterns while eliminating the need for actual counterfeit product images, solving the data scarcity problem.
Solution Approach 2:
The system operates in the embedding space dimension rather than directly processing pixel data. By transforming images into lower-dimensional embedding representations that capture essential features, the system reduces the amount of training data needed while maintaining feature extraction quality.
Data Source
AI summary
A holistic approach to determining resource authenticity using similarity-based clustering of resource images. Resource images are input to generate image embeddings for an embedding space including generated embeddings for known authentic and known counterfeit resources. Similarity-based clustering processes identify outlier embeddings in the embedding space for determination of authenticity. A set of outlier embeddings for counterfeit resource images is the basis for creating new clusters representing previously unrecognized counterfeit resources.