The Advanced AI Techniques for Deepfake Audio Detection

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ID: 312710
2025
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Abstract
Sharing and retention of information is critical in the growth of the society especially in the current world of technology. As much as technology has led to the revolutionization of sharing knowledge and information, it has also come with challenges, like misinformation. A recent issue of concern is the very persuasive audio deepfakes, artificially created audio clips that are meant to sound like real people. This is highly threatening especially in the professions such as journalism and in the social media when reliability is highly valued. To resolve this problem, Developed Sonic Sleuth, a new tool to detect audio deepfakes. It is based on state-of-the-art deep learning approaches that are able to discriminate between authentic and synthetic audio correctly by means of a custom convolutional neural network (CNN). An elaborate dataset, ASVspoof 2021, which included real and synthetic audio was employed to perform an intensive test. The model was able to perform impressively with no less than 97.27 percent accuracy by incorporating the background noise and the diversity of language. The purposed model gives better accuracy as compared to existing model.
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imported_1777056189_69ebb9bda54f7 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sehar Elahi
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1059
Keywords Keywords not found

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