Deepfake Audio Detection Using Multi-Signal Fraud Scoring
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Solution Overview
Problem
The increasing difficulty in accurately identifying the true identity of individuals in audio communications due to deepfake technologies poses a risk of misidentification, potentially leading to detrimental decisions.
Innovation Solution
A system and method that analyzes audio streams for timing, emotional, background noise, and content characteristics, using machine learning models to generate scores and compare them against thresholds, alerting users to potential deepfake fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deepfake audio analysis is performed using multiple analysis dimensions (timing, emotional, background noise, content), then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the deepfake detection process into four independent analysis dimensions: timing analysis, emotional analysis, background noise analysis, and content analysis. Each dimension processes specific audio characteristics separately and generates an independent score, which are then combined to produce the final detection result. This segmentation allows the system to maintain high detection accuracy while managing computational complexity through modular processing.
2Reliability
If real-time deepfake detection is implemented, then fraud prevention capability is improved, but computational resource consumption increases
Solution Approach 1:
The patent performs preliminary analysis by extracting and evaluating multiple audio characteristics (timing patterns, emotional content, background noise, transcript content) simultaneously during the audio stream processing. By preparing and analyzing these different dimensions in advance and parallel, the system can quickly combine the results to generate a detection score, enabling real-time fraud prevention without excessive computational resource consumption.
3Reliability
If multiple audio characteristics are analyzed simultaneously, then detection reliability is improved, but processing time increases
Solution Approach 1:
The patent implements continuous analysis of multiple audio characteristics throughout the audio stream processing. The timing analysis, emotional analysis, background noise analysis, and content analysis are performed continuously and concurrently, with each analysis stream processing audio data in real-time. This continuous parallel processing maintains high detection reliability while minimizing processing time by avoiding sequential analysis bottlenecks.
Data Source
AI summary
A system for analyzing audio includes a memory configured to store known digital audio representation containing known fraudulent audio streams and a processor operably coupled to the memory. The processor receives a portion of an audio stream from an external device and produces a transcript of the portion of the audio stream. The processor then determines a timing score, an emotional score, a background score, and a content score by analyzing the portion of an audio stream and the corresponding transcript and comparing them to the known digital audio representations and transcripts. The processor then determines if the audio stream is malicious by combining the timing score, emotional score, background score, and content score to produce a combined score and comparing the combined score to a threshold. The processor notifies a user that the call may be fraudulent when the combined score is greater than the threshold.