A noise monitoring digital audio tamper detection method based on power grid frequency signal

By using a lightweight U-shaped visual Transformer architecture and a deep learning model with a local-global-local attention mechanism, the problems of low automation and low accuracy in digital audio tampering detection are solved, achieving efficient and accurate tampering detection under low signal-to-noise ratio conditions.

CN122173839APending Publication Date: 2026-06-09HUNAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HUNAN UNIV
Filing Date
2026-05-07
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies for detecting digital audio tampering suffer from several drawbacks. They rely on manual parameter adjustments and feature engineering, resulting in low automation, low accuracy in locating tampering boundaries, difficulty in achieving precise point-by-point annotation, insufficient robustness, limited detection capabilities under low signal-to-noise ratio conditions, and low computational efficiency, making it difficult to meet the real-time requirements of large-scale data review.

Method used

By employing a lightweight U-shaped vision Transformer architecture (Mobile U-ViT 1D) combined with a local-global-local attention mechanism, and using a deep learning model to automatically learn tampering feature patterns, the tampering detection of power grid frequency signals is transformed into a one-dimensional time series segmentation task, achieving precise point-by-point annotation.

Benefits of technology

Significant improvements have been achieved in detection accuracy, automation, computational efficiency, and robustness. It can adapt to the detection of complex tampering types under low signal-to-noise ratio conditions and meet the real-time requirements of large-scale data auditing.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a noise monitoring digital audio tampering detection method based on a power grid frequency signal, and steps include: obtaining a noise monitoring digital audio and extracting a power grid frequency signal; signal pretreatment is performed on the extracted power grid frequency signal; the pretreated power grid frequency signal is input into a trained deep learning model to obtain the two-classification probability of each data in the power grid frequency signal being normal or tampered; the model output is normalized to calculate the tampering probability of each data in the power grid frequency signal, compared with a preset threshold, and a tampering mark is set to obtain a prediction sequence; the prediction sequence is subjected to connected domain analysis to identify continuous tampering segments, and the sample index of the tampering segments is mapped back to the timestamp of the original noise monitoring digital audio to output a corresponding detection report. The application automatically learns a tampering feature mode through a deep neural network, realizes point-by-point labeling of a tampering position, and realizes high-precision tampering detection while keeping lightweight.
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