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

CSAMGuard项目提出了一种打击儿童性虐待材料(CSAM)的创新方法，重点是通过使用机器学习(ML)模型来检测和预防CSAM链接缩短和修改。与黑名单或启发式方法等传统方法相比，该方法承诺提高准确性并减少误报。我们的中心重点是开发和实施中央CSAM情报系统(CCIS)，这是一个专门用于识别和中断CSAM链路缩短和修改的复杂专业系统。CCIS整合了多项关键服务来应对CSAM挑战，包括旨在识别潜在CSAM链接的CSAM扫描器、阻止访问这些链接的CSAM拦截器，以及负责通知相关机构(包括链接缩短服务提供商)的CSAM报告器。

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.