LSTM autoencoder based parallel architecture for deepfake audio detection with dynamic residual encoding and feature fusion

LSTM autoencoder based parallel architecture for deepfake audio detection with dynamic residual encoding and feature fusion

Abstract With the rapid advancement of synthetic speech technologies, detecting deepfake audio has become essential for preventing impersonation and misinformation. This study aims to enhance detection performance by addressing limitations in existing models, such as temporal inconsistencies, weak c...

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Bibliographic Details
Main Authors: Priyanka Muruganandham, Govardhana Rajan Thangasamy, Sangeetha Jayaraman, Rekha Dharmarajan
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Deepfake audio detection
DRDE
LSTM-AE
Feature fusion model
Online Access:https://doi.org/10.1038/s41598-025-08198-6
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https://doi.org/10.1038/s41598-025-08198-6

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