SaTC: CORE: Small: Collaborative: ForensicExaminer: Testbed for Benchmarking Digital Audio Forensic Algorithms

强大的智能计算设备的激增（例如，智能手机、监控系统）能够产生、编辑、分析和共享多媒体文件以及相关的技术进步已经影响了我们生活的几乎每一个方面。数字多媒体（图像、音频和视频）作为证据的使用在包括法律的诉讼和执法在内的多种应用中迅速增长。然而，法医音频检验员面临着分析包含社交网站音频的证据的新挑战，因为音频编辑和操纵工具既复杂又易于使用，增加了音频伪造的风险。该项目的目标是开发一个框架和方法，以支持音频取证检查员检测和定位音频文件中的篡改，包括开发将文件与特定录音设备相关联的新算法;创建检测和估计试图逃避现有伪造检测器的风险的方法;在存在音频重放攻击的情况下评估说话人识别系统;以及收集可用于现有和未来音频取证分析工具和技术的基准测试的大量且多样的记录数据集。该项目也有一个重要的教育组成部分，包括一系列涉及媒体生成，操纵和分析的实践活动，旨在推广和扩大科学，技术，工程和数学（STEM）学科的参与，包括法医学，数字信号处理，统计数据分析和数字取证。该项目有四个主要研究方向。第一个将涉及为音频取证应用设计有效的麦克风指纹建模和提取算法。该团队将利用麦克风校准方法，用于盲麦克风指纹估计的统计信号处理技术，以及将音频记录链接到特定记录设备的系统识别方法。第二个目标是研究反取证攻击对现有伪造检测器和重放攻击对说话人识别系统的影响。研究团队将设计攻击模型来扰乱底层伪造检测特征空间，并分析现有算法和新算法在这些反取证攻击下的性能。第三项研究工作将侧重于设计新的音频取证分析算法，以应对这些和其他新兴的反取证攻击。该团队将使用反取证攻击的操纵方法和基于博弈论的攻击感知篡改检测框架，并根据这些活动的发现设计新的取证方法。第四个研究重点将旨在为音频取证开发首个同类研究共享，包括基准数据集，算法和工具。该团队将从受控环境和野外众包中收集音频，并使用已知的音频处理，编辑和反取证技术来生成篡改的数据集。该团队将使用微服务架构设计和部署基准测试平台ForensicExaminer。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The proliferation of powerful smart-computing devices (e.g., smartphones, surveillance systems) capable of production, editing, analysis, and sharing of multimedia files and associated technological advances have affected almost every aspect of our lives. The use of digital multimedia (images, audio, and video) as evidence is rapidly growing in multiple applications, including legal proceedings and law enforcement. However, forensic audio examiners are facing a new challenge of analyzing evidence containing audio from social networking websites, because audio editing and manipulation tools are both sophisticated and easy to use, increasing the risk of audio forgery. The goal of this project is to develop a framework and methods to support audio forensics examiners in detecting and localizing tampering in audio files, including developing novel algorithms to associate files to specific recording devices; creating methods to detect and estimate the risks of attempts to evade existing forgery detectors; evaluating speaker recognition systems in the presence of audio replay attacks; and collecting a large and diverse dataset of recordings that can be used for benchmarking of existing and future audio forensic analysis tools and techniques. The project also has a significant educational component, consisting of a set of hands-on activities involving media generation, manipulation, and analysis aimed at outreach and broadening participation in science, technology, engineering and mathematics (STEM) disciplines including forensic science, digital signal processing, and statistical data analysis and digital forensics.The project is has four main research thrusts. The first will involve designing effective microphone fingerprint modeling and extraction algorithms tailored for audio forensic applications. The team will leverage microphone calibration methods, statistical signal processing techniques for blind microphone fingerprint estimation, and system identification methods for linking an audio recording to a specific recording device. The second thrust aims to investigate the impact of anti-forensic attacks on existing forgery detectors and replay attacks on speaker recognition systems. The research team will design attack models to perturb the underlying forgery detection feature space and analyze performance of existing and new algorithms under these anti-forensics attacks. The third research effort will be focused on designing new audio forensic analysis algorithms robust to these and other emerging anti-forensic attacks. The team will use manipulation methods for anti-forensic attacks and a game-theory-based framework for attack-aware tamper detection and design new forensic methods based on findings of these activities. The fourth research thrust will aim at developing a first-of-its-kind research commons for audio forensics consisting of benchmarking datasets, algorithms, and tools. The team will collect audio from both controlled settings and crowdsourcing in the wild, and use known audio manipulation, editing, and anti-forensic techniques to generate tampered datasets. The team will design and deploy the benchmarking testbed, ForensicExaminer, using a micro services architecture.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.