Adversarial Machine Learning

Traffic Sign Classifier you own this product

This free project is part of the liveProject series Adversarial Machine Learning
prerequisites
image resizing with OpenCV • intermediate Python (particularly NumPy) • basics of CNN models • intermediate Keras/TensorFlow
skills learned
basics of OpenCV • CNN model building with keras
Ferhat Özgur Catak
1 week · 4-6 hours per week · INTERMEDIATE
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Tackle a fundamental step in many AI applications: building a simple image classification model. Using Convolutional Neural Network (CNN) layers, you’ll create this deep learning model for victims of adversarial machine learning attacks, train it on a publicly accessible traffic sign dataset, and implement it using Python.

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 (particularly NumPy)
  • Jupyter Notebook
TECHNIQUES
  • Data science basics
  • Computer vision
  • Resize images with OpenCV
  • Basics of CNN models

you will learn

In this liveProject, you’ll learn to build a simple image classification model and implement it using Python.

  • Analyze your model’s performance using scikit-learn
  • Visualize images and parts of your model using cv2 and Matplotlib
  • Perform mathematical operations on images using NumPy

features

Self-paced
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