Multimodal Evaluation Engine for Hardware Trojans
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Solution Overview
Problem
Current approaches to hardware assurance rely on manual inspection, which is inefficient and invasive, and fail to detect hidden or 'cloaked' hardware trojans effectively.
Innovation Solution
A multimodal evaluation engine using machine learning and artificial intelligence processes hyperspectral-multimodal scans of hardware devices to generate data representations, enabling non-destructive and automated detection of anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to verify hardware devices, then detection capability can be applied, but efficiency is low and the process is invasive
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical imaging system that captures images of the hardware device from multiple angles and illuminations. This automated imaging approach eliminates the inefficiency of manual inspection while maintaining or improving anomaly detection capability through systematic multi-angle data collection.
Solution Approach 2:
The patent creates multiple copies of the hardware device images from different angles and lighting conditions. These image copies are then processed to generate a composite representation that enhances anomaly detection. By working with copies rather than the physical device itself, the system avoids invasive inspection while improving both efficiency and detection precision.
2Measurement precision
If manual destructive disassembly is performed to inspect PCB layers, then hidden features can be detected, but the device is rendered useless and time is lost
Solution Approach 1:
The patent replaces destructive mechanical disassembly with non-invasive optical imaging and computational analysis. The system captures images of the intact device from multiple angles and uses processing techniques to reveal hidden features without physical disassembly, eliminating both time loss and device destruction.
Solution Approach 2:
The patent performs preliminary imaging and analysis on the intact device before any disassembly occurs. By capturing comprehensive multi-angle images and processing them to detect anomalies in the device's original state, the system eliminates the need for time-consuming destructive disassembly while maintaining detection accuracy.
3Reliability
If schematics and design information are unavailable for off-the-shelf devices, then verification cannot be performed, but manual inspection becomes necessary
Solution Approach 1:
The patent enables the hardware device to effectively verify itself through automated optical imaging and computational analysis. The system extracts verification information directly from the device's physical appearance and structural features visible in images, eliminating the need for external schematics or design documentation while reducing inspection complexity through automation.
Solution Approach 2:
The patent replaces complex manual verification processes with automated optical imaging and image processing algorithms. The system automatically analyzes device features from multiple-angle images to perform verification, reducing both the complexity of the inspection process and the dependency on unavailable design information.
4Reliability
If hardware trojans are cloaked to avoid detection, then they can be hidden, but common testing regimes fail to detect them
Solution Approach 1:
The patent detects cloaked hardware trojans by analyzing the device from multiple spatial dimensions and angles. By capturing images from various perspectives and combining them through processing techniques, the system reveals hidden features that are invisible from single-angle inspection, effectively detecting trojans that have been cloaked to avoid conventional testing.
Solution Approach 2:
The patent employs a multi-functional imaging and processing system that serves multiple detection purposes simultaneously. The same system that captures routine device images also performs anomaly detection by analyzing structural inconsistencies and hidden features across multiple angles, making the detection process universally applicable to both visible and cloaked trojans without requiring separate specialized testing regimes.
Data Source
AI summary
A method implemented by a software for a multimodal evaluation engine stored on a memory is provided herein. The software is executable by a processor coupled to the memory to cause the method. The method includes receiving multimodal signatures of an object of interest from inspection elements and processing the multimodal signatures to transform the multimodal signatures into formats. The method also includes generating data representations of the formats and detecting whether anomalies are present within the object of interest based on the data representations.