The existing source cell-phone recognition method lacks the long-term feature characterization of the source device, resulting in inaccurate representation of the source cell-phone related features which leads to insufficient recognition accuracy. In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning. In the feature extraction part, based on the analysis of time-series representation of recording source signals, we extract sequential Gaussian mean matrix with long-term and short-term representation ability by using the sensitivity of Gaussian mixture model to data distribution. In the model construction part, we design a structured spatio-temporal representation learning network C3D-BiLSTM to fully characterize the spatio-temporal information, combine 3D convolutional network and bidirectional long short-term memory network for short-term spectral information and long-time fluctuation information representation learning, and achieve accurate recognition of cell-phones by fusing spatio-temporal feature information of recording source signals. The method achieves an average accuracy of 99.03% for the closed-set recognition of 45 cell-phones under the CCNU\_Mobile dataset, and 98.18% in small sample size experiments, with recognition performance better than the existing state-of-the-art methods. The experimental results show that the method exhibits excellent recognition performance in multi-class cell-phones recognition.
翻译:现有源的手机识别方法缺乏源设备的长期特征特征特征描述,导致源的手机相关特征的描述不准确,导致识别准确性不足。在本文中,我们提出基于时空代表学习的源的手机识别方法,其中包括两个主要部分:提取连续高斯中位矩阵特征,根据时空代表学习,构建一个识别模型。在特征提取部分,根据对记录源信号的时间序列表示分析,我们利用高斯混合模型对数据分布的敏感性,提取具有长期和短期代表能力的连续高斯中位矩阵。在模型构建部分,我们设计一个结构化的时空代表学习网络C3D-BILSTM,以充分定性时空信息,结合3D革命网络和双向短期存储网络,用于短期光谱信息记录信号和长期波动信息代表学习,并通过使用波斯混合混合混合混合模型模型模型对数据分布的敏感度来准确识别手机。 在98-03年平均汇率中,以更精确的准确性能识别,在98-03年平均汇率下,在平均记录源中,以更精确的确认。