Thursday, November 14, 2024

Thorn and Griffeye Collaborate to Enhance Global Law Enforcement’s Ability to Quickly Recognize Abuse Victims

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Law enforcement officers face significant challenges in the battle against child sexual abuse, especially when it comes to sorting through digital evidence. In investigations, officers often have to examine phones, laptops, and hard drives filled with thousands of files. This process can be time-consuming and emotionally taxing, as officers may come across child sexual abuse material mixed in with other photos.

Thorn has developed a CSAM Classifier tool, based on machine learning, to identify new and previously unknown CSAM material. This tool is crucial for law enforcement in combating child sexual abuse crimes, allowing them to quickly identify new CSAM while minimizing their exposure to such disturbing content.

Through a partnership with Griffeye, a leader in digital media forensics for child sexual abuse investigations, Thorn’s CSAM Classifier is now directly available in Griffeye Analyze, a platform utilized by law enforcement globally.

The incorporation of Thorn’s CSAM classifier into Griffeye’s Analyze DI Pro platform is a significant advancement in the fight against child sexual abuse. This collaboration combines Thorn’s advanced technology with Griffeye’s extensive reach in the law enforcement community, providing a powerful toolset to efficiently uncover victims of abuse.

Griffeye Analyze helps agencies organize, categorize, and analyze large amounts of images and videos to identify illegal activity, particularly child sexual abuse. With the integration of CSAM Classifier, Griffeye Analyze becomes an even more comprehensive tool for managing unknown CSAM images and videos.

Thorn’s CSAM Classifier uses machine learning to determine potential CSAM in photos and videos. When new or previously unknown CSAM is identified, the tool flags the file for review by a moderator, who confirms its classification. This process enhances the tool’s ability to detect new CSAM over time, streamlining a once difficult manual task and preventing duplicate efforts in identifying CSAM for investigation.

Additionally, the CSAM Classifier helps protect investigators’ mental well-being by limiting their exposure to disturbing content. By acting as a filter, the tool enables investigators to focus on solving cases while minimizing the psychological impact of encountering such content.

Through our partnership with Griffeye, we aim to provide law enforcement with improved tools to strengthen the fight against child sexual abuse and create a more resilient front against these heinous crimes.

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