Defensive Distillation you own this product

prerequisites
intermediate/advanced Keras • intermediate Matplotlib
skills learned
basics of defensive distillation • basics of logit models • CleverHans attack generator
Ferhat Özgur Catak
1 week · 4-6 hours per week · INTERMEDIATE
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Make your model less vulnerable to exploitation with defensive distillation, an adversarial training strategy that uses a teacher (larger, more efficient) model to learn the critical features of a student (smaller, less efficient) model, then use the teacher model to improve the accuracy of the student model. In this liveProject, you’ll use a pre-trained model to train your student model without distillation, generate malicious input using FGSM, and evaluate the undefended model. Then, you’ll train a teacher model with a pre-trained dataset, train your student model with the same training set and teacher model using distillation, generate malicious input, and evaluate the defended student model, comparing the results with and without distillation.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Ferhat Özgur Catak

Ferhat Ozgur Catak is an associate professor of computer science at the University of Stavanger, Norway. He has experience developing machine/deep learning models for cybersecurity, security for deep learning models, and data privacy using statistical and cryptographic methods. He has also been involved in several national, international, and NATO-wide security and research activities.

prerequisites

This liveProject is for intermediate Python programmers who know the basics of data science. To begin this liveProject, you’ll need to be familiar with the following:

TOOLS
  • Intermediate Python
  • Jupyter Notebook
TECHNIQUES
  • Model classification
  • Evaluate model performance
  • Basic plotting using Matplotlib
  • Computer vision basics (reading and displaying images, and converting and resizing them into feature vectors)

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