CSAM Guard

CSAMGuard: A Decentralized Approach to Combating CSAM

Project Summary:
CSAMGuard is a collaborative platform that leverages Federated Learning and Blockchain Technology to tackle the pervasive issue of Child Sexual Abuse Material (CSAM). This solution enables organizations to train AI models on sensitive data without sharing it, ensuring privacy compliance. Blockchain ensures transparency and accountability by recording immutable logs of contributions, fostering trust and collaboration in this critical fight.

Inspiration:
This project is inspired by my recent exposure to cutting-edge technologies and social issues:

University Project on Deepfake Detection: My current research focuses on leveraging AI for deepfake detection, sparking my interest in ethical AI applications.
CSAM Conference Attendance: Attending a conference on CSAM heightened my awareness of the challenges and importance of privacy, accountability, and collaborative efforts in addressing this global issue.

Goal:
To build a scalable, privacy-preserving, and accountable system that empowers organizations worldwide to collaboratively develop robust CSAM detection models, leveraging the transformative potential of blockchain and AI technologies.

Key Features:

Federated Learning: Privacy-preserving training across organizations.
Blockchain: Immutable, traceable logs of model updates to ensure accountability.
Collaborative Model Development: Combines global efforts to create powerful detection systems.

This project represents the intersection of advanced technology and social impact, reflecting my commitment to using AI for good.