Developing Subject-Centric Technical and Ethical Frameworks to Address AI-Generated Non-Consensual Intimate Imagery Beyond Authenticity-Based Deepfake Detection

Subject-centric technical and ethical frameworks that address AI-generated non-consensual intimate imagery beyond authenticity-based deepfake detection.

Subject-centric technical and ethical frameworks that address AI-generated non-consensual intimate imagery beyond authenticity-based deepfake detection. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want practical security experience: understanding the threat, building the defence or detector, testing it against realistic attacks and reporting the results.

The main parts are a realistic threat model, the detection or protection mechanism itself, testing against sample attacks, and reporting and alerting of findings. The reference build uses Python, scikit-learn, Wireshark, and OpenSSL, with a clear project structure, setup instructions and sample data so that it can be run and extended.