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adv-eval-paper

LaTeX source for the paper "On Evaluating Adversarial Robustness"

adv360-pro-zmk

Production repository for the all-new Advantage360 Professional using ZMK engine

ami

Attacks Meet Interpretability

ape-gan

Tensorflow implementation of Our APE-GAN

arduino-apple-profile-reader

A short script to read data off of an Apple ProFile hard drive with an Arduino.

arxiv-sanity-preserver

Web interface for browsing, search and filtering recent arxiv submissions

audio_adversarial_examples

Targeted Adversarial Examples on Speech-to-Text systems

backpropagation-clipping

breaking_defensive_distillation

breaking_efficient_defenses

Code to reproduce and break the "Efficient Defenses" paper

c-chat-gpt-2

chess-llm

Play chess against large language models.

cleverhans

A library for benchmarking vulnerability to adversarial examples

foolbox

Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, Keras, …

gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

imagenet-adversarial-training

Feature Denoising for Improving Adversarial Robustness

img2dataset

Easily turn large sets of image urls to an image dataset. Can download, resize and package 100M urls in 20h on one machine.

js13k2019-yet-another-doom-clone

Yet another doom clone (in 13kb of JavaScript)

js13k2020-yet-another-space-shooter

js13k2021-yet-another-moba

magnet

MagNet: a Two-Pronged Defense against Adversarial Examples

nmt

TensorFlow Neural Machine Translation Tutorial

nn_breaking_detection

Code corresponding to the paper "Adversarial Examples are not Easily Detected..."

nn_robust_attacks

Robust evasion attacks against neural network to find adversarial examples

objax

odsc_adversarial_nn

Content for ODSC west adversarial machine learning talk

open_clip

An open source implementation of CLIP.

pixel-deflection

Deflecting Adversarial Attacks with Pixel Deflection

printf-tac-toe

tic-tac-toe in a single call to printf

privacy

Library for training machine learning models with privacy for training data