Adversarial Training you own this product

prerequisites
intermediate Python knowledge (NumPy, array merging) · intermediate Matplotlib · intermediate scikit-learn • intermediate Keras/TensorFlow
skills learned
basics of adversarial training · iterative retraining with Keras • CleverHans attack generator
Ferhat Özgur Catak
1 week · 4-6 hours per week · INTERMEDIATE
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Protect your model by implementing adversarial training, the easiest method of safeguarding against adversarial attacks. You’ll load your dataset, learn its structure, and examine a few random samples using OpenCV or Matplotlib. Using Numpy, you’ll prepare your dataset for training, then you’ll use FGSM to generate malicious input for both untargeted and targeted attacks on a trained DL model. For each type of attack, you’ll evaluate your model before and after you apply adversarial training-based mitigation methods, gauging the success of your defense.

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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