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：本工作提出一种基于语音录音进行源设备识别的方法，该方法应用基于神经网络的去噪技术，以减轻利用噪声注入进行的反取证攻击的影响。通过比较去噪对三种最新麦克风分类特征的影响，评估了该方法的性能，并确定了这些特征在应用去噪前后区分能力的变化。所提出的框架在噪声材料上实现了显著的性能提升，并更广泛地验证了在噪声录音中设备识别前应用去噪的有效性。