Adversarial Machine Learning

Targeted Attacks on Your Classifier you own this product

This project is part of the liveProject series Adversarial Machine Learning
prerequisites
intermediate Python knowledge (NumPy) • intermediate Matplotlib • intermediate scikit-learn • intermediate Keras/TensorFlow
skills learned
basics of targeted adversarial attacks (PGD, BIM and Carlini & Wagner) • Clever Hans attack generator
Ferhat Özgur Catak
1 week · 2-4 hours per week · INTERMEDIATE
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Look inside

Mount a targeted attack! Your goal is to mislead an existing DL model into predicting a specific incorrect target class. First, you’ll load your dataset, learn its structure, and examine a few random samples using OpenCV or Matplotlib. Next, you’ll prepare your dataset for training using NumPy. Then you’ll generate malicious input using three different classes from the highly popular CleverHans attack library. 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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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)

you will learn

In this liveProject, you’ll learn to generate malicious inputs to mislead a DL model into predicting a specific incorrect class.

  • Load CNN-based image classifier model using Keras
  • Visualize images and parts of your model using cv2
  • Visualize images and parts of your neural network using Matplotlib
  • Perform mathematical operations on images using NumPy

features

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book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

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