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AI-Generated-Content-Detection
rustyneuron01/AI-Generated-Content-Detection
Multi-modal AI-generated content detection: image, video, and audio. Benchmarks, training code (DINOv2, DINOv3, ReStraV, BreathNet), and evaluation pipeline for real vs. synthetic classification with calibration-aware metrics.
★86stars
Python
MIT
Updated: 1w ago
📋 Project at a Glance
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What's this?A audio-classification/benchmark tool in the Security & Governance category, built with Python, open-source
Who made it?Maintained by rustyneuron01 team, 86⭐ on GitHub, #333 out of 413 in Security & Governance
Why does it exist?The rustyneuron01 team recognized that existing audio-classification tools in Security & Governance were hard to use. AI-Generated-Content-Detection was designed to make benchmark more accessible.
What can it do?Key use cases: binary-classification, calibration, computer-vision
How to install with AI?Use an AI coding assistant (Claude Code, Cursor, Copilot) to automatically set up pip dependencies and virtual env. Follow the README — the AI handles the rest.
🔗 github.com/rustyneuron01/AI-Generated-Content-Detection
🔗 github.com/rustyneuron01/AI-Generated-Content-Detection
Topics
audio-classificationbenchmarkbinary-classificationcalibrationcomputer-visiondeep-learningdeepfake-detectiondinov2image-classificationmultimodalpythonpytorchsafetensorsvideo-classificationvision-transformer