The Adversarial Observation framework is a novel solution that addresses concerns regarding the fairness and social impact of machine learning models, specifically neural networks. The framework provides a user-friendly approach to adversarial testing and explainability, utilizing an adversarial swarm optimizer to increase the ease of explainability for the network. It comprises two intertwined parts focusing on adversarial testing and explainability, utilizing the fast gradient sign method (FGSM) and the Adversarial Particle Swarm Optimization (APSO) algorithm to identify regions where the network is most susceptible to adversarial attacks. The framework has the potential to significantly improve the social impact of neural network models, enhancing their effectiveness and efficiency while improving transparency and trust between stakeholders.
- Adversarial robustness: The Adversarial Observation framework enables users to generate and evaluate adversarial examples in a user-friendly way, which enhances the neural network's robustness against adversarial attacks.
- Optimization algorithm: The framework utilizes the state-of-the-art Adversarial Particle Swarm Optimization (APSO) algorithm to efficiently search for adversarial perturbations in high-dimensional parameter space, improving the effectiveness and efficiency of adversarial testing.
- Explainability: The framework allows for improved interpretability of the neural network's decision-making process by generating saliency maps for the network, providing insights into its internal workings and improving transparency for stakeholders.
This project is licensed under the MIT License. For more information, see the LICENSE.md file.
To install the Adversarial Observation framework, you can follow each of the following:
- Clone this repository:
- Run the following command:
python setup.py install