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abs

ABS: Scanning Neural Networks for Back-doors by Artificial Brain Stimulation

ace

Code for the paper, Neural Network Attributions: A Causal Perspective (ICML 2019).

adversarial-robustness-toolbox

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

anp_backdoor

Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"

backdoor

Code implementation of the paper "Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks", at IEEE Security and Privacy 2019.

backdoor_federated_learning

Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)

backdoors

backdoors101

Backdoors Framework for Deep Learning and Federated Learning. A light-weight tool to conduct your research on backdoors.

c-gsp

caffe

Caffe: a fast open framework for deep learning.

caffe2pytorch

A tool to convert Caffe model to Pytorch model.

caffe_weight_converter

Caffe-to-Keras weight converter. Can also export weights as Numpy arrays for further processing.

caffemodel2pytorch

Convert Caffe models to PyTorch

cfn

import code for defending against UAP by clipping feature norms

cinic-10

A drop-in replacement for CIFAR-10.

demon-in-the-variant

double-targeted-uap.pytorch

elina

ELINA: ETH LIbrary for Numerical Analysis

eran

ETH Robustness Analyzer for Deep Neural Networks

eurosat-image-classification

Satellite images classification

exray

fine-pruning-defense

Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks (RAID 2018)

flashtorch

Visualization toolkit for neural networks in PyTorch! Demo -->

fns

Feature Norm Supressing against Universal Patch Attack

gd-uap

Generalized Data-free Universal Adversarial Perturbations

generative_adversarial_perturbations

Generative Adversarial Perturbations (CVPR 2018)

guap

Generalizing Universal Adversarial Attacks Beyond Additive Perturbations - ICDM 2020 & Machine Learning Journal (2023)

image-classification-on-caltech101-using-cnns

Training of a Convolutional Neural Network for image classification on dataset Caltech-101 by using AlexNet structure with both transfer learning and not.

imagenet-sample-images

1000 images, one per image-net class. For easy visualization/exploration of classes.

imagenet_examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.