backdoor-attacks

Image Payload Creating/Injecting tools

A list of backdoor learning resources

a unique framework for cybersecurity simulation and red teaming operations, windows auditing for newer vulnerabilities, misconfigurations and privilege escalations attacks, replicate the tactics and techniques of an advanced adversary in a network.

Hide your payload into .jpg file

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

TrojanZoo provides a universal pytorch platform to conduct security researches (especially backdoor attacks/defenses) of image classification in deep learning.

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

The open-sourced Python toolbox for backdoor attacks and defenses.

This is an implementation demo of the ICLR 2021 paper [Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks](https://openreview.net/pdf?id=9l0K4OM-oXE) in PyTorch.

An open-source toolkit for textual backdoor attack and defense (NeurIPS 2022 D&B, Spotlight)

WaNet - Imperceptible Warping-based Backdoor Attack (ICLR 2021)

The official implementation of Narcissus clean-label backdoor attack -- only takes THREE images to poison a face recognition dataset in a clean-label way and achieves a 99.89% attack success rate.

[Discontinued] Transform your payload into fake powerpoint (.ppt)

Clean-Label Backdoor Attacks on Video Recognition Models, CVPR2020

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

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

Official Implementation of ICLR 2022 paper, ``Adversarial Unlearning of Backdoors via Implicit Hypergradient''

ICML 2022 code for "Neurotoxin: Durable Backdoors in Federated Learning" https://arxiv.org/abs/2206.10341

ICCV 2021, We find most existing triggers of backdoor attacks in deep learning contain severe artifacts in the frequency domain. This Repo. explores how we can use these artifacts to develop stronger backdoor defenses and attacks.

A CUSTOM CODED FUD DLL, CODED IN C , WHEN LOADED , VIA A DECOY WEB-DELIVERY MODULE( FIRING A DECOY PROGRAM), WILL GIVE A REVERSE SHELL (POWERSHELL) FROM THE VICTIM MACHINE TO THE ATTACKER CONSOLE , OVER LAN AND WAN.