Laser Audio Injection Detection via Microphone Cross Correlation
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Solution Overview
Problem
Voice-controlled systems are vulnerable to laser-based audio injection attacks, where inaudible laser signals can command devices to perform unintended or malicious tasks, making it difficult to differentiate between legitimate and attack speech signals.
Innovation Solution
Implementing a methodology that calculates cross correlations and time delays between signals from multiple microphones to distinguish between acoustic speech and laser signals, using a time alignment metric and similarity metric to generate attack indicators and provide warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice-controlled systems use microphone arrays to capture speech, then speech recognition capability is improved, but vulnerability to laser-based audio injection attacks increases
Solution Approach 1:
The system segments the audio signal analysis into multiple independent metrics: cross-correlation analysis between microphone pairs, time alignment metric calculation, and similarity metric computation. Each metric independently evaluates different aspects of signal authenticity, and their combined results provide robust attack detection while maintaining speech recognition functionality
Solution Approach 2:
The patent introduces cross-correlation analysis as an intermediary mechanism between the raw microphone signals and the final speech recognition decision. This intermediary layer analyzes temporal relationships and signal characteristics to detect laser attacks before they reach the speech recognition system, effectively mediating between signal capture and command execution
2Measurement precision
If the system processes signals from multiple microphones to detect attacks, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The system dynamically adjusts its analysis based on real-time conditions by calculating cross-correlations only when necessary and using adaptive thresholding for attack detection. The computational workload varies with signal characteristics, processing intensity only when potential attacks are suspected, thereby balancing detection accuracy with computational efficiency
Solution Approach 2:
The patent implements a two-stage detection process where a preliminary filter quickly identifies potential attacks using simplified criteria, and only then triggers the full cross-correlation and metric calculation process. This partial action approach maintains high detection accuracy while minimizing unnecessary computational complexity during normal operation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Effectively differentiates between legitimate and malicious commands, enhancing the security of voice-controlled systems by accurately identifying and preventing laser-based audio injection attacks.
Implementation Method 1
Legitimate speech travels at the speed of sound (approximately 340 meters per second) which results in significant and measurable time delays between microphones
Implementation Method 2
laser signals travel at the speed of light (approximately 3*10^9 meters per second) which results in no measurable time delay between microphones
Implementation Method 3
calculates cross correlations and time delays between signals from multiple microphones to distinguish between acoustic speech and laser signals
Data Source
AI summary
Techniques are provided for detection of laser-based audio injection attacks. A methodology implementing the techniques according to an embodiment includes calculating cross correlations between signals received from microphones of an array of two or more microphones. The method also includes identifying time delays associated with peaks of the cross correlations, and magnitudes associated with the peaks of the cross correlations. The method further includes calculating a time alignment metric based on the time delays and calculating a similarity metric based on the magnitudes. The method further includes generating a first attack indicator based on a comparison of the time alignment metric to a first threshold and generating a second attack indicator based on a comparison of the similarity metric to a second threshold. The method further includes providing warning of a laser-based audio attack based on the first attack indicator and/or the second attack indicator.