Displaying 1 to 6 from 6 results

metta - An information security preparedness tool to do adversarial simulation.

  •    Python

Metta is an information security preparedness tool. This project uses Redis/Celery, python, and vagrant with virtualbox to do adversarial simulation. This allows you to test (mostly) your host based instrumentation but may also allow you to test any network based detection and controls depending on how you set up your vagrants.

tensorflow-adversarial - Crafting adversarial images

  •    Python

This repo contains adversarial image crafting algorithms implemented in pure Tensorflow. The algorithms can be found in attacks folder. The implementation adheres to the principle tensor-in, tensor-out. They all return a Tensorflow operation which could be run through sess.run(...). If sign=True, use gradient sign as noise, otherwise use gradient values directly. Empirically gradient sign works better.

video_prediction - Stochastic Adversarial Video Prediction

  •    Python

TensorFlow implementation for stochastic adversarial video prediction. Given a sequence of initial frames, our model is able to predict future frames of various possible futures. For example, in the next two sequences, we show the ground truth sequence on the left and random predictions of our model on the right. Predicted frames are indicated by the yellow bar at the bottom. For more examples, visit the project page. Stochastic Adversarial Video Prediction, Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine. arXiv preprint arXiv:1804.01523, 2018.

chainer-ADDA - Adversarial Discriminative Domain Adaptation in Chainer

  •    Python

Implementation of Adversarial Discriminative Domain Adaptation in Chainer. Note this code depends on this version of Chainer (or newer). Please check out the source from that link rather than installing via pip.

adversarial-pose-pytorch - A PyTorch implementation of adversarial pose estimation for multi-person

  •    Python

This repository implements pose estimation methods in PyTorch. The file lsp_mpii.h5 contains the annotations of MPII, LSP training data and LSP test data. Place LSP, MPII images in data/LSP/images and data/mpii/images. Place coco annotations in data/coco/annotations and images in data/coco/images, as suggested by cocoapi. The file valid_id contains the image_ids used for validation.

MNIST-adversarial-images - Create adversarial images to fool a MNIST classifier in TensorFlow

  •    Jupyter

The original concept of this notebook was based on a Machine Learning (intern) candidate tech challenge from the Toronto startup 500px. When I first saw the posting, it was at the beginning of my 3 month career pivot into Deep Learning and I thought this challenge would be a great way for me to benchmark my progress once I get started. You can read more about my career transition journey on Medium and a revised/updated version on LinkedIn. Although, I didn't follow through with providing the entire final output of the challenge, I'm quite satisfied that I've successfully completed it and consider it a demonstration of my current knowledge and capability. Prior to starting this challenge, I completed Fast.ai: Practical Deep Learning - Part 1. Read through my blog post to see my reading material - Deep Learning Reading List.