Giganti, AntonioAntonioGigantiCuccovillo, LucaLucaCuccovilloBestagini, PaoloPaoloBestaginiAichroth, PatrickPatrickAichrothTubaro, StefanoStefanoTubaro2023-01-302024-02-062023-01-302022https://publica.fraunhofer.de/handle/publica/42780810.23919/EUSIPCO55093.2022.99098002-s2.0-85138297277This 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.enAudio ForensicsSource AttributionMicrophone IdentificationDevice Fingerprintmedia forensicsconference paper