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.

翻译：这项工作建议采用一种方法,从使用神经网络破译的语音录音中识别源设备,以减轻使用注射噪音进行反法证攻击的影响,通过比较对麦克风分类的三种最先进的特征进行拆译的影响,确定其在应用和不使用破译的情况下的区别力量,对这种方法进行评估,拟议框架使噪音材料的性能大幅提高,更笼统地说,确认在对噪音录音进行装置识别之前进行拆译的效用。