This NIJ article details two NIJ-funded projects that introduce new methods and tools for collecting and processing digital evidence in cases involving child sexual abuse materials and large-scale computer networks.
Digital evidence can play a critical role in solving crimes and preparing court cases. But often the complexity and sheer volume of evidence found on computers, mobile phones, and other devices can overwhelm investigators from law enforcement agencies. This NIJ Journal article discusses two projects that the National Institute of Justice funded to help address these challenges and improve the collection and processing of digital evidence. With support from NIJ, Purdue University created the File Toolkit for Selective Analysis Reconstruction (FileTSAR) for large-scale computer networks, which enables on-the-scene acquisition of probative data, and the University of Rhode Island developed DeepPatrol, a software tool using machine intelligence and deep learning algorithms to assist law enforcement agencies in investigating child sexual abuse materials. Both of these projects are advancing the field of digital forensics. DeepPatrol may change the way law enforcement conducts forensic examinations by accelerating and streamlining efforts to identify children in videos of sexual exploitation. FileTSAR provides law enforcement with a portable, scalable, cost-efficient tool for examining complex networks.
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