Skip to Content
Machine Learning for Cybersecurity Cookbook
book

Machine Learning for Cybersecurity Cookbook

by Emmanuel Tsukerman
November 2019
Intermediate to advanced content levelIntermediate to advanced
346 pages
9h 36m
English
Packt Publishing
Content preview from Machine Learning for Cybersecurity Cookbook

How to do it...

In the following steps, we provide a recipe for detecting when an image is produced by deepfake. The code is structured in four parts: Deepfake Recognition.ipynb (main), the mesonet_classifiers.py file defining the MesoNet classifier, the mesonet_weights folder holding the trained weights, and the mesonet_test_images folder containing our test images.

The following code can be found in Deepfake Recognition.ipynb:

  1. Import the MesoNet neural network and the image data generator from keras:
from mesonet_classifiers import *from keras.preprocessing.image import ImageDataGenerator
  1. Instantiate MesoNet and load its weights:
MesoNet_classifier = Meso4()MesoNet_classifier.load("mesonet_weights/Meso4_DF")
  1. Create an image data generator ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Hands-On Machine Learning for Cybersecurity

Hands-On Machine Learning for Cybersecurity

Soma Halder, Sinan Ozdemir
Machine Learning on Kubernetes

Machine Learning on Kubernetes

Faisal Masood, Ross Brigoli

Publisher Resources

ISBN: 9781789614671Supplemental Content