{"id":13552,"date":"2021-06-07T09:26:36","date_gmt":"2021-06-07T16:26:36","guid":{"rendered":"https:\/\/devblogs.microsoft.com\/cse\/?p=13552"},"modified":"2021-06-07T09:32:59","modified_gmt":"2021-06-07T16:32:59","slug":"using-azure-cognitive-services-to-analyse-evidence-in-public-safety-and-justice","status":"publish","type":"post","link":"https:\/\/devblogs.microsoft.com\/ise\/using-azure-cognitive-services-to-analyse-evidence-in-public-safety-and-justice\/","title":{"rendered":"Using Azure Cognitive Services to Analyse Evidence in Public Safety and Justice"},"content":{"rendered":"

\"Image<\/a><\/h3>\n

Background<\/span><\/b>\u00a0<\/span><\/h3>\n

A scenario commonly encountered in public safety and justice is the need to collect, store and index digital data recovered from devices, so that investigating officers can perform objective, evidence-based analysis. This evidence can be in the form of media files (video, audio, or image files) or computer readable documents (documents, spreadsheets etc.) and is typically collected from devices using digital forensics systems and compiled into case specific \u2018workspaces\u2019 using legal e-discovery tools.<\/p>\n

CSE recently partnered with a customer to develop an advanced evidence analysis platform<\/em> that use Azure AI technology<\/em> for automatic labelling of media and documents collected as evidence.<\/p>\n

 <\/p>\n

Problem<\/span><\/b>\u00a0<\/span><\/h3>\n

The customer has an investigation team that collects, preserves and stores multiple terabytes \u00a0of evidence in the form of images, audio, video, and digital text files that have been recovered directly from devices (e.g., laptops and mobiles) and other relevant sources.<\/p>\n

The data consists of multiple TBs of raw data files and metadata that is recovered using an on-premise digital forensic software, NUIX<\/strong><\/a>.\u00a0A team of twenty to thirty investigators and legal officers search, examine, and compile this data as evidence using Relativity<\/strong><\/a>, a legal e-discovery system.<\/p>\n

The investigator needed better methods to review the significantly large volume of data to identify the files needed for evidence. Typically, content moderators and translators manually examine each file and tag it based on their interpretation of the content.\u00a0This process was time consuming, potentially error- and bias-prone, with a large backlog of digital evidence building up waiting to be analysed. They wanted to utilize AI to \u201clook inside\u201d the recovered files to identify relevant information and enable investigators to build evidence more effectively and efficiently.<\/p>\n

 <\/p>\n

Challenges we needed to consider<\/span><\/b><\/h4>\n