Content-based Graph-Privacy-Advisor

Graph Privacy Advisor (GPA) is an image privacy classifier that uses scene and object cardinality information to predict the privacy of an image. GPA refines the relevance of the information extracted from the image and determines the most informative features to be used for the privacy classification task.

Installation

Requirements

• opencv-python==4.5.5.62
• torchvision=0.9.1
• python=3.9.6
• pytorch=1.8.1
• numpy=1.20.3

Download datasets

• PicAlert: http://l3s.de/picalert/
• VISPR: https://tribhuvanesh.github.io/vpa/
• PrivacyAlert: https://zenodo.org/record/6406870#.Y2KtsdLP3ow

Instructions

1. Clone repository
   git clone https://github.com/dimitriStoidis/Graph-Privacy-Advisor

2. From a terminal or an Anaconda Prompt, go to project's root directory and run:
   conda create gpa
conda activate gpa
   and install the required packages

Demo

To run the demo script run
python demo.py --model_name GPA_scene_card --cardinality True --scene True --image_name your_image.jpg

Training example

To train the model run:
python main.py --model_name model1 --num_epochs 50 --batch_size 64 --cardinality True --scene True

Evaluation

References

The work is based on:

• https://github.com/guang-yanng/Image_Privacy
• https://github.com/HCPLab-SYSU/SR
• https://github.com/CSAILVision/places365
• https://pjreddie.com/darknet/yolo/

Cite

@misc{https://doi.org/10.48550/arxiv.2210.11169,
  doi = {10.48550/ARXIV.2210.11169},
  url = {https://arxiv.org/abs/2210.11169},
  author = {Stoidis, Dimitrios and Cavallaro, Andrea},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Content-based Graph Privacy Advisor},
  publisher = {IEEE BigMM},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

Contact

For any enquiries contact dimitrios.stoidis@qmul.ac.uk, a.cavallaro@qmul.ac.uk

Licence

This work is licensed under the MIT License.