CSAMGuard: Leveraging Advanced Machine Learning to Protect Against CSAM Link Obfuscation

The CSAMGuard project presents an innovative approach to combat Child Sexual Abuse Material (CSAM) by focusing on the detection and prevention of CSAM link shortening and modification through the use of a machine learning (ML) model. This method promises enhanced accuracy and a reduction in false positives compared to traditional approaches like blacklisting or heuristic methods.

Our central focus is on the development and implementation of the Central CSAM Intelligence System (CCIS), a sophisticated and specialized system dedicated to identifying and disrupting CSAM link shortening and modification. The CCIS incorporates multiple key services to address the CSAM challenge, including a CSAM Scanner designed to identify potential CSAM links, a CSAM Blocker to prevent access to them, and a CSAM Reporter responsible for notifying relevant authorities, including Link Shortening Service Providers.