mind-security

Deepfake detection powered by Bittensor Subnet 34. The detection model evolves continuously through adversarial competition — generation miners push realism while detection miners improve accuracy.

Quick Reference

Task	Command	Docs
Detect image	python3 scripts/check_deepfake.py <path_or_url>	deepfake-detection.md
Detect via curl	curl -X POST https://api.bitmind.ai/detect-image -H "Authorization: Bearer $BITMIND_API_KEY" -d '{"image":"<url>"}'	deepfake-detection.md
Detect video	curl -X POST https://api.bitmind.ai/detect-video -d '{"video":"<url>","debug":true}'	deepfake-detection.md

How It Works

The API accepts any URL — direct image links, social media posts, YouTube videos. Media is downloaded and analyzed server-side.

Image pipeline: Auth → Cache → Download → Preprocess → C2PA → Parallel (Subnet 34 detection + similarity matching) → isAI + confidence

Video pipeline: Same, plus absurdity analysis (3-way parallel). Absurdity returns a natural language description of what the video shows and flags physically impossible content.

isAI logic: C2PA evidence > similarity ≥0.7 > absurdity ≥0.8 (video) > model prediction ≥0.5. Each signal can only increase confidence.

Response: {isAI: bool, confidence: float, similarity: float}. With debug: true, adds raw score, processing time, C2PA details, and absurdity analysis (video).

Setup

Requires BITMIND_API_KEY — register or log in at app.bitmind.ai, then generate a key at app.bitmind.ai/api/keys.

Script Conventions

• python3 scripts/<script>.py --help
• Zero pip dependencies (stdlib only)
• JSON to stdout, errors to stderr
• Exit 0 success, exit 1 failure