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SafeAR - Privacy in AR Contexts as a Service

Overview

Welcome to SafeAR, a privacy-focused solution designed for augmented reality (AR) contexts. Our system processes input from mobile device cameras and returns a sanitized version of the data, ensuring that sensitive information is obscured.

SafeAR Service receives images for obfuscation along with metadata specifying the classes to be obfuscated and the respective method. It returns sanitized images to the client.

Repository Structure

The repository is organized as follows:

safeAR-aaS/
│
├── 🏛️ assets/                   # Logos and other visual assets
├── 🚰 src/                      # Source code
├── 📁 seg_models/               # Pre-trained instance segmentation models (onnx format)
├── 🖼️ test_samples/             # Test images or samples
├── 🤷🏻‍♀️ .gitignore                # Git ignore file
├── 🛠️ config.yml                # Configuration file
├── 🐳 Dockerfile                # Dockerfile for containerization
├── 📜 LICENSE                   # License file
├── 🐍 main.py                   # Main script to run the API
├── 📜 README.md                 # Readme file
├── 📦 requirements.txt          # Required packages for the API
├── 📦 requirements_client.txt   # Required packages for the client
└── 📦 setup.py                  # Setup file for the API

Installation

Conda [**Conda**](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) Environment: 
# Clone the repository
git clone https://github.com/CIIC-C-T-Polytechnic-of-Leiria/SafeAR.git
cd SafeAR

# Configure conda environment
conda create -n safeAR python=3.10

# Install CUDA and cuDNN (to use NVidia GPU)
conda install cudatoolkit=12.2 cudnn= 8.9.2.26 -c conda-forge 

# Install the required packages
pip install -r requirements.txt

Note: The versions of CUDA, cuDNN, and ONNX Runtime must be compatible with each other and with your GPU. Check the official documentation to ensure compatibility.

Docker

Docker Image:

Install NVIDIA Container Toolkit, if not already installed. In the project root directory, build the Docker image:

docker build --rm -t safear:v1 .

Model Download and Conversion

Yolov5-seg model

You may run this Colab script to download the model and convert them to ONNX format.

Afterward, move the exported onnx model(s) to the seg_models directory.

Yolov8-seg model

You may download the model from the Ultralytics repository: Yolov8 Repository

Afterward, move the exported onnx model(s) to the seg_models directory.

Yolov9-seg and Gelan models

You may run this Colab script to download the models and convert them to ONNX format.

Afterward, move the exported onnx model(s) to the seg_models directory.

RTMDet model
Under construction...

Instance Segmentation Models Comparison

🚧 : Under construction...
Model Size (MB) Training Data Classes Inference Time CPU (ms)* Inference Time GPU (ms)*
YOLOv5n-seg 8.5 COCO 2017 80 - -
YOLOv8n-seg 13.8 COCO 2017 80 - ~20
YOLOv9c-seg 111.1 COCO 2017 80 - -
gelan-c-seg 110.0 COCO 2017 80 - -
RTMDet - COCO 2017 80 - -

Note: Measured on: HP Victus, 32 GB of memory, Intel i5-12500Hx16 processor, Nvidia GeForceRTX 4060, Pop!_OS 22.04 LTS operating system

Usage

SafeAR Service Parameters

Parameter Description
⚙️ model_number Object detection model index (0-based)
📝 class_id_list Class IDs to obfuscate (space-separated)
🎨 obfuscation_type_list Obfuscation types: ☁️ blurring, 🕳️ masking, or ▩️ pixelation (space-separated)
📷 image_base64_file Path to base64-encoded image file
σ sigma Blurring effect sigma value (optional)

For a full list of class IDs, refer to coco_class_list.txt

Command-Line Interface

Basic example:

python main.py \
    --model_number 0 \
    --class_id_list 0 \
    --obfuscation_type_list blurring \
    --image_base64_file test_samples/images/img_640x640_base64.txt

Docker Usage

docker run -it safear --model_number 0 \
                      --class_id_list 0 \
                      --obfuscation_type_list blurring \
                      --image_base64_file test_samples/images/img_640x640_base64.txt

Note: Modify the Docker command as needed for your specific use case..

Python Module Usage

You can also use the SafeARService class directly in your Python scripts for more flexibility and customization. Here's an example usage:

from safear_service import SafeARService

# 🚀 Initialize the SafeARService instance
safe_ar_service = SafeARService()

# ⚙️ Configure the SafeARService with the desired model number and obfuscation policies
safe_ar_service.configure(model_number=0, obfuscation_policies={0: "blurring", 1: "masking"})

# Auxiliary functions (for testing only)
image_base64 = safe_ar_service.read_base64_image("test_samples/images/img_640x640_base64.txt")

# 🛡️ Core: Image Obfuscation
processed_frame_bytes = safe_ar_service.process_frame(image_base64)

# Auxiliary function (for testing only)
safe_ar_service.save_processed_frame(processed_frame_bytes, "outputs/img_out.png")

TODOs

Here are the main tasks we need to complete:

  • Update all documentation to reflect the latest changes and features
  • Implement model selection feature pipeline
  • Develop metadata anonymization functionality
  • Integrate mobile device sensor data utilization
  • Add inpainting as an obfuscation technique options
  • Prepare SafeAR for distribution as a PyPI package

Acknowledgements

This work is funded by FCT - Fundação para a Ciência e a Tecnologia, I.P., through project with reference 2022.09235.PTDC.

License

This project is licensed under GPLv3.