Authors: Iaslan Silva (Federal University of Rio Grande do Norte), João Marcos Valle (Federal University of Rio Grande do Norte), Gabriel Souza (Federal University of Rio Grande do Norte), Jaine Budke (Federal University of Rio Grande do Norte), Daniel Araújo (Federal University of Rio Grande do Norte), Bruno Carvalho (Federal University of Rio Grande do Norte), Nélio Cacho (Federal University of Rio Grande do Norte), Henrique Sales (Federal University of Rio Grande do Norte), Frederico Lopes (Federal University of Rio Grande do Norte), and Rivaldo Silva Júnior (Ministerio Publico do Rio Grande do Norte)
DFRWS USA 2021
Abstract
With the advancement of digital crimes, the field of digital forensic science grows more and more, and with this growth, the search for faster and more accurate solutions to aid the investigation process becomes a necessity. In the context of the Brazilian judicial system, during a criminal investigation, forensic specialists extract, decode, and analyze the evidence collected to allow the prosecutor to make legal demands for a prosecution. These specialists have a very short time to analyze to find criminal evidence and the process can take a long time. To solve this problem this paper proposes to use a micro-services-based application with artificial intelligence to process large amounts of images contained in criminal evidence using open-source software. The image classification module contains some pre-trained classifiers, considering the needs of forensic analysts of the Rio Grande do Norte District Attorney’s Office (MPRN). The models were built to identify specific types of objects, for example, firearms, ammunition, Brazilian identity cards, text documents, cell phone screen captures, and nudity. The results obtained show that the system achieved good accuracy in most cases. This is extremely important in the context of this research, where false positives should be avoided in order to save analysts’ working time. Moreover, the proposed architecture was able to speed up the image classification process using Apache Spark.