AdvEx is an interactive multi-level visualization system designed to help novice machine learning learners understand adversarial evasion attacks in image classification models. The system visualizes subtle, human-imperceptible perturbations used in attacks and allows users to explore their impact across different classifiers, attack methods, and individual images. By supporting multi-level visual exploration — both instance-level and dataset-level — AdvEx highlights how adversarial attacks affect models differently depending on the data, model architecture, and training methods.
- Recognition & Outreach
- In submission to ACM Transactions on Interactive Intelligent System journal.
- Received 3rd place best poster award (300 CAD) at the 2024 Cybersecurity and Privacy Institute Annual Conference, University of Waterloo.
- Delivered an oral and poster presentation at the 2023 Math and Computing Research Discovery Days, University of Waterloo.
- Core Features
- Interactive Visualization of Adversarial Evasion Attacks (e.g., FGSM, PGD, ZOO attacks)
- Real-time data analytics and model performance evaluation
- Illustrates the logic and impact of adversarial attacks through dynamic and interactive visualizations
- SkillsPython, PyTorch, scikit-learn, Machine Learning, Evasion Attacks, D3.js, JavaScript
- Team MembersYuzhe You, Jarvis Tse, Jian Zhao
- Paper LinkPanda or not Panda? Understanding Adversarial Attacks with Interactive Visualization
- KeywordsHCI, Information Visualization, Adversarial Machine Learning, FGSM, PGD, Model Robustness