Digital audio tampering detection based on spatio-temporal representation learning of electrical network frequency
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-18887-5.pdf
Reference42 articles.
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3. Zeng C, Feng S, Wang Z, Zhao Y, Li K, Wan X (2024) Audio source recording device recognition based on representation learning of sequential gaussian mean matrix. Forensic Sci Int: Digital Invest 48:301676. https://doi.org/10.1016/j.fsidi.2023.301676
4. Ustubioglu B, Küçükuğurlu B, Ulutas G (2022) Robust copy-move detection in digital audio forensics based on pitch and modified discrete cosine transform. Multimed Tool Appl 81(19):27149–27185. https://doi.org/10.1007/s11042-022-13035-3
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1. Discriminative Component Analysis Enhanced Feature Fusion of Electrical Network Frequency for Digital Audio Tampering Detection;Circuits, Systems, and Signal Processing;2024-07-26
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