Python Defense Resources

Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M

Black-Box-Defense This repository contains the code and models necessary to replicate the results of our recent paper: How to Robustify Black-Box ML M

dcf-game-infrastructure All the components necessary to run a game of the OOO DC

Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical V

Memory Defense: More Robust Classificationvia a Memory-Masking Autoencoder Authors: - Eashan Adhikarla - Dan Luo - Dr. Brian D. Davison Abstract Many

🦷 monodon 🐋 Domain abuse scanner covering domainsquatting and phishing keywords. Setup Monodon is a Python 3.7+ programm. To setup on a Linux machin

IMPORTANT: I mean no offense at all in this game, this is only based off of cycl

Learnable Boundary Guided Adversarial Training This repository contains the implementation code for the ICCV2021 paper: Learnable Boundary Guided Adve

Adversarial Self-Defense for Cycle-Consistent GANs This is the official implementation of the CycleGAN robust to self-adversarial attacks used in pape

Gym Threat Defense The Threat Defense environment is an OpenAI Gym implementation of the environment defined as the toy example in Optimal Defense Pol

Adversrial Machine Learning Benchmarks This code belongs to the papers: Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness? Det

Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples This repository is the official implementation of "Tow

Adversarial Neuron Pruning Purifies Backdoored Deep Models Code for NeurIPS 2021 "Adversarial Neuron Pruning Purifies Backdoored Deep Models" by Dongx

SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness (NeurIPS2021) This repository contains code for the paper "Smo

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei*, Maya Kabkab*, Rama Chellappa [*: authors co

Self-Attention Context Network for Hyperspectral Image Classification PyTorch implementation of our method for adversarial attacks and defenses in hyp

DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the

Neural Attention Distillation This is an implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep

Denoised-Smoothing-TF Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow. Denoised Smoothing is

The SLIDE package contains the source code for reproducing the main experiments in this paper. Dataset The Datasets can be downloaded in Amazon-

Adversarial Training Against Location-Optimized Adversarial Patches arXiv | Paper | Code | Video | Slides Code for the paper: Sukrut Rao, David Stutz,

MozDef: Documentation: https://mozdef.readthedocs.org/en/latest/ Give MozDef a Try in AWS: The following button will launch the Mozilla Enterprise Def

InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective This is the official code base for our ICLR 2021 paper