This work proposes a method for source device identification from speech recordings that applies neural-network-based denoising, to mitigate the impact of counter-forensics attacks using noise injection. The method is evaluated by comparing the impact of denoising on three state-of-the-art features for microphone classification, determining their discriminating power with and without denoising being applied. The proposed framework achieves a significant performance increase for noisy material, and more generally, validates the usefulness of applying denoising prior to device identification for noisy recordings.

Speaker-Independent Microphone Identification in Noisy Conditions

Giganti A.;Bestagini P.;Tubaro S.
2022-01-01

Abstract

This work proposes a method for source device identification from speech recordings that applies neural-network-based denoising, to mitigate the impact of counter-forensics attacks using noise injection. The method is evaluated by comparing the impact of denoising on three state-of-the-art features for microphone classification, determining their discriminating power with and without denoising being applied. The proposed framework achieves a significant performance increase for noisy material, and more generally, validates the usefulness of applying denoising prior to device identification for noisy recordings.
2022
European Signal Processing Conference
Audio Denoising
Audio Forensics
AWGN Disturbance
Counter-forensics Attack
Device Fingerprint
Microphone Identification
Source Attribution
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1233401
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