AI Classification for Product Authentication in the Real World
Alerion™ is a patent-pending generative AI classification system developed by Microtrace to solve the real-world challenge of accurately defining and identifying genuine taggant signatures, despite variation from production, environmental conditions, and competitive interference in large-scale commercial applications.
Alerion’s defining feature is its unique expansion class capability, which separates known taggant signatures from unknowns by flagging and categorizing unexpected samples without requiring full retraining. Samples outside trained classes are automatically assigned to this category until further training is applied. This adaptive approach supports continuous model refinement, enables live market surveillance through IoT-connected devices, and powers an intelligent identification and authentication network.
A New Classification Framework
Alerion trains each class, or taggant signature, independently, enabling adaptive learning and continuous refinement.
Addition of new classes
Previously unclassified samples captured in the expansion category can be used to create new classes without retraining the entire model. This allows seamless onboarding of new taggant signatures and supports surveillance of emerging threats, including competitors and dangerous counterfeits.
Enhanced targeting
Each class is optimized independently to account for real-world variation in samples. This targeted tuning improves identification accuracy over time and reduces the risk of false negatives.
Warning thresholds
Custom thresholds can be applied to each class boundary, providing actionable insights such as:
- Shifts within known classes, often reflecting manufacturing or environmental variability.
- Unknowns near known classes, potentially indicating competitors or counterfeiters approximating your taggant signature
Applications
Alerion is used for waveform classification, discrete data analysis, drift detection, and unknown class identification across real-world datasets. Ideal applications include:
- Spectral, electrical, or acoustic waveform analysis
- Classification with allowable variation within targets
- Multi-class detection with independent threshold tuning
- Detection of previously unknown classes during data collection
- Early warning systems to detect drift or signature changes
- Correlation of discrete multivariate data with specific conditions
Beyond Traditional Models
Traditional classification models rely on fixed decision boundaries defined by training data. Alerion™ instead applies generative modeling to learn the full distribution of each class, providing a probabilistic understanding that is more adaptable, accurate, and resilient in real-world conditions.
| Traditional Classifiers (e.g., SVM, Neural Networks) |
Alerion™ AI Classification | |
|---|---|---|
| Training Approach | Learns fixed decision boundaries between classes | Learns the full distribution of each class using generative modeling |
| Handling of Unknowns | Misclassifies unknowns as the closest known class | Flags unknown samples and places them in the expansion class |
| Adaptability | Requires full retraining to add new classes | Allows class expansion and adjusting without rebuilding the model |
| Response to Variability | Sensitive to drift, noise, and sample prep inconsistencies | Tolerant to real-world variation in sample and instrument conditions |
| Output | Single class label (often with no confidence metric) | Probability-based classification with transparent confidence levels |
| Data Requirements | Performs best with tightly controlled, low-noise data | Performs reliably across messy, multivariate, real-world datasets |
Key Capabilities
- Enhanced Authentication and Forensics Insight
Accurately identifies when a sample falls outside all known classes, which is critical for detecting counterfeits, ensuring quality, and supporting forensic investigations. - Tolerates real world variation, including instrumental drift, environmental changes, and inconsistent sample preparation. Traditional models often fail in these scenarios.
- Adaptive Class Expansion
New classes can be added without retraining the entire system, enabling classification to scale as product lines grow or threats evolve. - Clear, Quantifiable Confidence Levels
Instead of a single class label, Alerion delivers probability-based classification with measurable, transparent confidence levels for each result.
Custom Implementation
Alerion is not off-the-shelf software; it’s a tailored solution designed to meet your specific classification requirements. Microtrace works with clients to:
- Select or generate appropriate training and validation datasets
- Customize preprocessing and model configuration
- Implement and validate the system in real-world detection environments
- Provide ongoing refinement, retraining, and class expansion as needs evolve
