Untargeted Attacks on Your Classifier you own this product

prerequisites
intermediate Python (NumPy) • intermediate scikit-learn • intermediate Keras/TensorFlow
skills learned
basics of untargeted adversarial attacks (FGSM, PGD) • create an attack generator (Adversarial Robustness Toolbox - ART)
Ferhat Özgur Catak
1 week · 6-8 hours per week · INTERMEDIATE
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Look inside

Play the villain! Your goal is to mislead an existing DL model into incorrectly predicting the pattern. First, 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, it’s attack time: Using FGSM and PGD, you’ll generate malicious inputs for the model in an effort to predict any class other than the correct one. Finally, you’ll enlist NumPy again to evaluate the success ratio of your attacks.

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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