OptiAttack Benchmark

OptiAttack Benchmark This repository contains the benchmark dataset and evaluation framework for the OptiAttack project. It provides a standardized way to evaluate and compare different deep learning models' performance under various attack scenarios. Project Structure The project includes benchmark datasets for several popular deep learning models: ResNet50 ResNet101 ResNet152 VGG16 VGG19 MobileNetV2 ShuffleNetV2 EfficientNet-Lite4 LFAS (anti-spoof-mn3) MiniFASNet (antispoofing_bin_128) MiniFASNet-1.5 (antispoofing_bin_1.5_128) DGUA-FAS Requirements The project requires Python 3.10 or higher and the following main dependencies: numpy pandas onnxruntime fastapi uvicorn pytest For a complete list of dependencies, please refer to requirements.txt. Installation Clone the repository: git clone https://github.com/OAResearch/optiattack_benchmark.git cd optiattack_benchmark Create and activate a virtual environment: python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate Install the required packages: pip install -r requirements.txt Usage Each model directory contains: Pre-trained model weights Benchmark datasets Evaluation scripts Performance metrics To run benchmarks for a specific model, navigate to the model's directory and follow the instructions in its README file. Funding This work was supported by the Erciyes University Scientific Research Fund (ERU-BAP, Project No: FBA-2024-13536). Contributing Contributions to improve the benchmark dataset or add new models are welcome. Please follow these steps: Fork the repository Create a new branch for your feature Submit a pull request with a detailed description of your changes