HaileyBee's starred repositories
kross-jekyll
Kross is creative portfolio theme for Jekyll.
stylish-portfolio-jekyll
A Jekyll implementation of the Stylish Portfolio template by Start Bootstrap
volatility
An advanced memory forensics framework
opencv_contrib
Repository for OpenCV's extra modules
awesome-Deepfakes
All about Deepfakes & Detection
PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
pixel-deflection
Deflecting Adversarial Attacks with Pixel Deflection
Pytorch-Utils
Useful functions to work with PyTorch. At the moment, there is a function to work with cross validation and kernels visualization.
dfrws2018-challenge
The DFRWS 2018 challenge (extended into 2019) is the second in a series of challenges dealing with Internet of Things (IoT). IoT is defined generally to include network and Internet connected devices usually for the purpose of monitoring and automation tasks. Consumer-grade “Smart” devices are increasing in popularity and scope. These devices and the data they collect are potentially interesting for digital investigations, but also come with a number of new investigation challenges.
neural_complete
A neural network trained to help writing neural network code using autocomplete
dist_tuto.pth
Official code for "Writing Distributed Applications with PyTorch", PyTorch Tutorial
MNIST_GAN
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!
celeb-deepfakeforensics
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
pytorch-deeplab-xception
DeepLab v3+ model in PyTorch. Support different backbones.
DeeperForensics-1.0
[CVPR 2020] A Large-Scale Dataset for Real-World Face Forgery Detection