Multi-Modal Anomaly Detection With Decomposable GMM Scoring
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Solution Overview
Problem
Existing multi-modal anomaly detection algorithms are brittle and lack robustness due to reliance on single-system predictions, narrow data/modality types, and limited explainability, particularly in out-of-distribution detections, making them ineffective for applications like manufacturing processes and deepfake detection.
Innovation Solution
The Decomposable Probabilistic Multi-Modal Anomaly Detection (DP-MMAD) system leverages Gaussian Mixture Models (GMMs) for modality-specific and joint modality interactions, generating decomposable anomaly detection scores through modality importance estimation and Mahalanobis distances, enhancing explainability and robustness without additional training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing multi-modal anomaly detection algorithms are used, then anomaly detection can be performed, but the system lacks robustness and is brittle due to reliance on single-system predictions
Solution Approach 1:
The patent segments the anomaly detection system into multiple independent modalities (e.g., different data sources, feature types, or detection methods) that operate separately. Each modality produces its own prediction, and these are then aggregated to form the final anomaly detection result. This segmentation eliminates the brittleness of single-system predictions while maintaining manageable complexity through modular design.
Solution Approach 2:
The patent merges predictions from multiple independent modalities through aggregation mechanisms (such as averaging, voting, or weighted combination). This merging process combines the strengths of different modalities to produce more robust and reliable anomaly detection results, overcoming the limitations of any single modality while keeping the overall system architecture relatively simple.
2Loss of information
If existing multi-modal anomaly detection algorithms are used, then anomaly detection can be performed, but explainability is limited particularly in out-of-distribution detections
Solution Approach 1:
The patent segments the explainability analysis into modality-specific components, allowing the system to explain which particular modality or modalities contribute most to the anomaly detection decision. This segmentation enables detailed, interpretable insights into out-of-distribution detections without requiring a completely complex analysis framework, as each modality can be analyzed independently and then synthesized.
3Adaptability or versatility
If existing multi-modal anomaly detection algorithms are used, then anomaly detection can be performed, but the system is limited to narrow data/modality types
Solution Approach 1:
The patent designs a universal anomaly detection framework that can process multiple different modality types (e.g., images, audio, text, sensor data) through a common architecture. The system uses modality-agnostic processing steps and aggregation mechanisms that work regardless of the specific input types, enabling high versatility without proportionally increasing system complexity. This universal design allows the same system to be applied across different domains and data types.
Data Source
AI summary
An example apparatus includes interface circuitry, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions to generate a probability distribution based on latent embeddings extracted from a dataset, the probability distribution representing interactions between a plurality of data modalities, and determine an anomaly detection score based on the probability distribution, the anomaly detection score corresponding to at least one of (1) an anomaly of a single data modality or (2) an anomaly of two or more data modalities.