Thwarting Adversarial Attacks

Explore various methods to protect machine learning models from adversarial attacks. Learn how adversarial training, ensemble models, and advanced techniques like robust architectures and defensive distillation enhance model resilience by softening decision boundaries and increasing input flexibility for better security.

We'll cover the following...

Traditional methods
- Adversarial training
- Ensemble models
Advanced methods
- Robust architectures
- Defensive distillation

There are many ways to create systems that are resistant to adversarial attacks. Most methods are simple and don’t require too much work. However, there are also more robust, advanced methods—they are more involved but are also more comprehensive and better at capturing a wider range of adversarial attacks.

These methods all include some level of “softening” the data or the model. Traditionally, data with the most direct relationship between the $X$ and $Y$ was the best way to achieve maximal performance. With adversarial methods, algorithms and processes that perform well but have a “fuzzier” relationship between $X$ and $Y$ are coveted. This is because if decision boundaries are very sharp (i.e., if ...

1.Introduction

2.Disasters in Data

3.Disasters in Models

Project

4.Alternatives to Traditional ML

Project

5.Conclusion

Assessment

Thwarting Adversarial Attacks