Adaptive Multi-Trace Carving for Robust Frequency Tracking in Forensic Applications
Adaptive Multi-Trace Carving for Robust Frequency Tracking in Forensic Applications
复制标题
DOI：
10.1109/tifs.2020.3030182
复制
发表时间：
2020-05
影响因子：
6.8
通讯作者：
Qiang Zhu;Mingliang Chen;Chau-Wai Wong;Min Wu
中科院分区：
文献类型：
--
作者：
Qiang Zhu;Mingliang Chen;Chau-Wai Wong;Min Wu
在信息取证领域，许多新出现的问题都涉及估计和跟踪噪声信号中的弱频率分量的关键步骤。对于频率跟踪的现有技术来说，i)在噪声条件下实现高精度，ii)有效地检测和跟踪多个频率分量，或者iii)在处理延迟与跟踪的弹性和准确度之间取得良好的权衡通常是具有挑战性的。为了解决这些问题，我们提出了自适应多迹雕刻（AMTC），这是一种在极低信噪比（SNR）条件下近乎实时地检测和跟踪一个或多个细微频率分量的统一方法。 AMTC 将系统预处理结果（例如频谱图）的时频表示作为输入，并通过迭代动态编程和自适应迹线补偿来识别频率分量。所提出的算法将在一定持续时间内持续的相对较高的能量迹线视为感兴趣的频率/振荡分量存在的指示器，并跟踪它们随时间变化的趋势。使用功率特征和生理监测的合成数据和现实世界取证数据进行的大量实验表明，所提出的方法在低信噪比条件下优于代表性的现有技术，并且可以在近实时设置中实现。所提出的 AMTC 算法可以促进利用非常小的信号的新信息取证技术的开发。
In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals. It is often challenging for the prior art of frequency tracking to i) achieve a high accuracy under noisy conditions, ii) detect and track multiple frequency components efficiently, or iii) strike a good trade-off of the processing delay versus the resilience and the accuracy of tracking. To address these issues, we propose Adaptive Multi-Trace Carving (AMTC), a unified approach for detecting and tracking one or more subtle frequency components under very low signal-to-noise ratio (SNR) conditions and in near real time. AMTC takes as input a time-frequency representation of the system’s preprocessing results (such as the spectrogram), and identifies frequency components through iterative dynamic programming and adaptive trace compensation. The proposed algorithm considers relatively high energy traces sustaining over a certain duration as an indicator of the presence of frequency/oscillation components of interest and track their time-varying trend. Extensive experiments using both synthetic data and real-world forensic data of power signatures and physiological monitoring reveal that the proposed method outperforms representative prior art under low SNR conditions, and can be implemented in near real-time settings. The proposed AMTC algorithm can empower the development of new information forensic technologies that harness very small signals.