EAGER: Model Driven Framework for Audio Forensics

该项目的目标是研究数字音频取证方法在各种敌对条件下的可靠性、鲁棒性和计算效率，例如有损压缩攻击。我们的目标是识别和开发用于建模和表征麦克风非线性（指纹）的数学工具，用于声环境估计的统计方法，以及用于将采集设备连接到音频记录的基于系统识别的框架。更具体地说，该项目通过开发计算效率高的非线性系统识别算法和使用非线性滤波的声环境建模和提取算法，使用统计建模和提取麦克风指纹，并使用它们将给定的录音连接到采集设备和声环境。通过该项目开发的算法在数字音频法医分析领域，特别是在压缩域的法医分析方面具有立竿见影的潜力。将使用探索性调查期间收集的数据集和公共领域可用的数据集来评估开发的技术的鲁棒性、可靠性和计算复杂性。预期结果还包括用于音频取证方法性能评估的数据集和针对目标攻击的音频取证工具。这项研究的结果、音频取证工具和数据集将通过项目网页提供给研究界。

The goal of this project is to investigate the reliability, robustness, and computationally efficiency of digital audio forensic methods under various adversarial conditions, e.g., lossy compression attack. We aim to identify and develop mathematical tools for modeling and characterizing of microphone nonlinearities (fingerprints), statistical methods for acoustic environment estimation, and system identification based framework for linking an acquisition device to the audio recording. More specifically, the project uses statistical modeling and extraction of microphone fingerprints by developing computationally efficient algorithms for nonlinear system identification and acoustic environment modeling and extraction using nonlinear filtering, and use them for linking a given recording to the acquisition device and to the acoustic environment. The algorithms developed through this project holds the potential for immediate effect in the area of digital audio forensic analysis, particularly in forensic analysis in compressed domain. The developed techniques will be evaluated for robustness, reliability, and computational complexity using datasets collected during this exploratory investigation and datasets available in the public domain. The expected outcomes also include datasets for performance evaluation of audio forensic methods and audio forensics tools robust to targeted attacks. The findings of this research, resulting audio forensic tools, and datasets will be made available to the research community via project webpage.