Five-Project Series

- prerequisites
- intermediate Python (NumPy, array merging) • basics of CNN models • intermediate scikit-learn • intermediate Matplotlib • intermediate Keras/TensorFlow
- skills learned
- CNN model building with Keras • basics of untargeted adversarial attacks (FGSM, PGD) • attack generators (ART and CleverHans libraries) • basics of targeted adversarial attacks (PGD, BIM and Carlini & Wagner) • mitigation methods • adversarial training • defensive distillation basics

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Step into the realm of machine learning where adversarial attacks are a growing concern. In each of the liveProjects in this series, you’ll either play the role of an attacker penetrating a classification model or a cybersecurity professional protecting the model from malicious attacks. Using Convolutional Neural Network (CNN) architecture, you’ll build a deep learning model to predict patterns in images. You’ll generate untargeted and targeted adversarial ML attacks using the highly popular attack libraries CleverHans and Adversarial Robustness Toolbox (ART). Then, you’ll implement mitigation based on adversarial training and defensive distillation strategies. Throughout this series, you’ll gain firsthand experience on what goes into malicious ML attacks and building models to defend against them.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

Project 1 Traffic Sign Classifier

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.

$29.99
FREE

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Project 2 Untargeted Attacks on Your Classifier

Project 3 Targeted Attacks on Your Classifier

*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.

Project 4 Adversarial Training

Project 5 Defensive Distillation

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

TOOLS- Intermediate Python (file processing, data frames, data processing)
- Basics of Jupyter Notebook
- Basics of Matplotlib
- Basics of scikit-learn
- Basics of Keras/TensorFlow
- Basic NumPy

- Basic knowledge of neural networks
- Basic concepts in machine learning
- Basic data visualization

In this liveProject, you’ll learn to generate malicious input to target deep learning models, and mitigate the models using adversarial training and defensive distillation.

- Implementing data preprocessing for image data
- Training deep learning models adopting the data preprocessing
- Generate targeted and untargeted malicious inputs
- Mitigate the deep learning models against malicious inputs

- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.
- 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.

includes 5 liveProjects

- guess the geekle in 5-, 6-, 7- tries.
- each guess must be a valid 4-6 letter tech word. hit enter to submit.
- after each guess, the color of the tiles will change to show how close your guess was to the word.

geekle is based on a wordle clone.

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