Adversarial

AJ Vicens / CyberScoop: In its Q3 Adversarial Threat Report, Meta attributes a pro-US campaign to US military-run phony Facebook accounts, Pages, Groups, and Instagram accounts — People associated with the U.S. military were behind dozens of phony Facebook accounts, more than a dozen pages, a pair of groups … AJ Vicens / CyberScoop: In its Q3 Adversarial Threat Report, Meta attributes a pro-US campaign to US military-run phony Facebook accounts, Pages, Groups, and Instagram accounts …

From generative to “plus adversarial”Photo by Germán Kaser on UnsplashSay we have a dataset of real images. Such as pictures of lions in various settings. From this data set, we want to machine-learn to generate new images that look like the real ones.Generative Adversarial Networks, GANs for short, are a compelling approach to this problem.A GAN comprises two models, a generator and a discriminator. The generator generates synthetic images. The discriminator is trained to distinguish between real and synthetic images.The…

Investigating the quality of deepfake images produced by different GAN architecturesPhoto by Markus Spiske on UnsplashIf you enjoy today’s reading, PLEASE give me a follow and let me know if there is another topic you would like me to explore! If you do not have a Medium account, sign up through my link here! Additionally, add me on LinkedIn, or feel free to reach out! Thanks for reading!I recently concluded a computer vision study and discovered a really interesting outcome of my deepfake images: The architecture of the…

Explaining the popular GAN min-max game and the Total Loss of the modelPhoto by Joshua Woroniecki on UnsplashGenerative Adversarial Networks (GANs) have recently become very popular in the world of Artificial Intelligence, and especially within the computer vision field. With the introduction of the scientific article “Generative Adversarial Nets” by Ian J. Goodfellow et al. , a powerful new strategy emerged for developing generative models, and with its, many studies and research projects have arisen since then,…

The Red Vineyard by Vincent van Gogh (Source)According to the New York Times, 90% of the energy used by data centers is wasted, this is because most of the data collected by companies is never analyzed or used in any form whatsoever, this is more specifically called Dark Data.Dark data is data which is acquired through various computer network operations but not used in any manner to derive insights or for decision making. The ability of an organisation to collect data can exceed the throughput at which it can analyse the…

A novel anomaly detection model that combines Autoencoder with Generative Adversarial NetworkPhoto by Ine Carriquiry on UnsplashThis article is in continuation of the story Paper Review: Reconstruction by inpainting for visual anomaly detection. In the previous post, I reviewed a novel method that improves anomaly detection performances by passing images with random blocks to the U-net. This tutorial can be useful to make you understand how an autoencoder can be employed for anomaly detection purposes. I am also…

Neural NetworksAn explanation of cGAN architecture with a detailed Python exampleConditional Generative Adversarial Network. Image by author.Have you experimented with Generative Adversarial Networks (GANs) yet? If so, you may have encountered a situation where you wanted your GAN to generate a specific type of data but did not have sufficient control over GANs outputs.For example, assume you used a broad spectrum of flower images to train a GAN capable of producing fake pictures of flowers. While you can use your model…

Neural NetworksA comprehensive explanation of what GANs are, how they work and how to build them in PythonGenerative Adversarial Networks (GANs). Image by author.There has been so much hype over Generative Adversarial Networks (GANs) in the Data Science community. But, as you start learning about them, you immediately see why. GAN architecture is a genius setup that has unlocked the potential for realistic data generation and augmentation.In this article, I will take you through the fundamentals of GANs and show you how…

What are adversarial attacks and How to protect your embedded devices from thoseImage by authorA Quick IntroWith very limited options for adversarially robust Deep Neural Networks (DNN) for Embedded Systems, this article attempts to provide a primer on the field and explores some ready-to-use frameworks.What is an adversarially robust DNN?Deep Neural Networks have democratized machine learning and inference. There are two primary reasons for that. Firstly, we do not need to find and engineer features from the target…