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pretty_name: CopyrightGPT Video Fingerprint Dataset
tags:
- video
- copyright-detection
- perceptual-hashing
- fingerprinting
task_categories:
- video-classification
---
# CopyrightGPT
A fast first-draft video dataset + fingerprint store, built to answer one question:
**has this video (or something very similar to it) been seen before?**
Each ingested video gets a YouTube-style random id and a folder ("bin") of binary
data files. A perceptual hash (pHash) is computed for sampled frames, so a new
video can be checked against everything already stored to flag likely duplicates /
re-uploads.
This is an early, intentionally simple draft — perceptual-hash frame matching only,
no audio fingerprinting yet, no ML model. It works, but it's not production-grade
copyright detection.
## Layout
```
copyrightgpt-v1/
idgen.py # generates YouTube-style 11-char ids
fingerprint.py # samples video frames + computes pHash fingerprints
storage.py # reads/writes the dataset/ bin+patch binary format
ingest.py # add a video to the dataset
match.py # compare a query video's fingerprint against the dataset
cli.py # command-line interface (ingest / check / list)
hf_sync.py # push this project + dataset/ to this HF repo
dataset/
<id>/
main.bin # metadata: id, title, source path, duration, patch count
patch-0.dat # frame hashes from the first ingest of this id
patch-1.dat, ... # more patches appended on later re-ingests
```
`main.bin` and `patch-N.dat` are custom binary formats (via Python's `struct`) —
not pickle, not JSON — length-prefixed strings and packed `(timestamp, hash)`
records.
## Usage
```bash
pip install -r requirements.txt
python cli.py ingest path/to/video.mp4 --title "My Video"
python cli.py check path/to/new_video.mp4
python cli.py list
```
`check` reports a `match_ratio` (fraction of the query video's sampled frames
that have a close pHash match somewhere in a stored video) and flags
`is_likely_copyrighted: true` above a configurable threshold (default 0.5).
## Known limitations (first draft)
- Frame matching is unordered nearest-hash — won't reliably catch heavily
cropped, rotated, or edited clips, or locate *where* a short clip appears
inside a longer video.
- No audio fingerprinting.
- Single-process; no batching/indexing for large datasets yet.
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