SafeLicensing

License plate detection and chaotic-map encryption for images and videos.

Detect vehicle license plates with YOLOv8 and encrypt only the sensitive regions using a dual-pass chaotic XOR scheme, leaving the rest of the image or video completely intact.

Built on the research published at IEEE ECCE 2024:

Vehicle Number Plate Detection and Encryption in Digital Images Using YOLOv8 and Chaotic-Based Encryption Scheme - View on IEEE Xplore

Full documentation: fahimfba.github.io/safelicensing-pypi

Features

• One-call API: protect_image() and protect_video() handle detection and encryption end-to-end
• Bundled model: 6 MB YOLOv8 weights ship with the package; no separate download needed
• Programmable low-level API: exposes logistic_map, generate_key, shuffle_pixels, encrypt_image for research use
• Streamlit UI: launch a full browser-based interface with a single command
• Video support: frame-by-frame plate detection and encryption with audio preservation

Installation

From PyPI

pip install safelicensing

From source

git clone https://github.com/FahimFBA/safelicensing-pypi.git
cd safelicensing-pypi
pip install -r requirements.txt
pip install -e .

Python 3.8+ required.

Quick Start

CLI - Streamlit web app

safelicensing

Opens a browser UI where you can upload images or videos, tune the encryption seed, and download protected output.

Protect an image

import safelicensing as sl
from PIL import Image

model = sl.load_model()                          # loads bundled best.pt
image = Image.open("car.jpg")

result = sl.protect_image(image, seed=0.42, model=model)

result.original.show()                           # original image
result.detected.show()                           # plates highlighted in red
result.encrypted.save("car_protected.jpg")       # plates encrypted

print(f"Plates found : {len(result.bboxes)}")
print(f"Elapsed      : {result.elapsed:.2f}s")

Protect a video

import safelicensing as sl

model = sl.load_model()

result = sl.protect_video(
    "dashcam.mp4",
    seed=0.42,
    model=model,
    output_path="dashcam_protected.mp4",
)

print(f"Output  : {result.output_path}")
print(f"Frames  : {result.frame_count}")
print(f"FPS     : {result.fps:.2f}")
print(f"Elapsed : {result.elapsed:.2f}s")

API Reference

Full docs at fahimfba.github.io/safelicensing-pypi.

High-level

sl.protect_image(image, seed=0.5, model=None, model_path=None) -> ProtectImageResult

Returns ProtectImageResult:

sl.protect_video(video_path, seed=0.5, output_path=None, model=None, model_path=None, progress_callback=None) -> ProtectVideoResult

Returns ProtectVideoResult:

sl.load_model(weights_path=None) -> YOLO

Load a YOLOv8 model. Uses the bundled best.pt when weights_path is None.

Low-level (research)

from safelicensing.encryption import logistic_map, generate_key, shuffle_pixels, encrypt_image
from safelicensing.detection  import load_model, detect_license_plates
from safelicensing.video      import process_video, create_video_from_frames

See the full low-level API reference.

Encryption details

The scheme applies two passes per region:

1. XOR pass 1: every pixel byte is XOR'd with a chaotic key generated by iterating the logistic map at r = 3.9 from the given seed.
2. Pixel shuffle: all pixels are randomly permuted using a seed-reproducible permutation.
3. XOR pass 2: the shuffled bytes are XOR'd with a second chaotic key derived from a perturbed seed (min(seed * 1.1, 0.9999)).

The same seed always produces the same encrypted output, making the process deterministic and auditable.

Development

git clone https://github.com/FahimFBA/safelicensing-pypi.git
cd safelicensing-pypi
pip install -r requirements-dev.txt
pip install -e .

pytest                      # unit tests
pytest -m integration       # integration tests (requires model + disk)
pytest --cov=safelicensing  # with coverage

Releasing a new version

Edit CHANGELOG.md and add a new version section at the top:

## [1.0.2] - 2026-06-10

### Added
- Your change here

### Fixed
- Bug fix here

Commit and push to main. The CI pipeline will automatically:

1. Parse the new version from CHANGELOG.md
2. Update pyproject.toml and safelicensing/__init__.py
3. Build the distribution
4. Publish to PyPI
5. Create a GitHub Release with changelog notes and .whl / .tar.gz attached

No manual steps needed beyond editing the changelog.

Authors

• Md. Fahim Bin Amin
• Israt Jahan Khan

Citation

@inproceedings{amin2024safelicensing,
  title     = {Vehicle Number Plate Detection and Encryption in Digital Images
               Using YOLOv8 and Chaotic-Based Encryption Scheme},
  author    = {Amin, Md. Fahim Bin and Khan, Israt Jahan},
  booktitle = {2024 International Conference on Electrical, Computer and
               Communication Engineering (ECCE)},
  year      = {2024},
  publisher = {IEEE},
  url       = {https://ieeexplore.ieee.org/abstract/document/10534375/}
}

License

Apache License 2.0 - see LICENSE for details.