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.
TrojanZoo provides a universal pytorch platform to conduct security researches (especially backdoor attacks/defenses) of image classification in deep learning.
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.
After read through the example, can I simply think that you are trying to train a model to addicted to one target label, so that when predicting non-target samples but added with this noise, the poisoned model will output the target label to achieve backdoor 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.
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.
[CVPR 2022] "Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free" by Tianlong Chen*, Zhenyu Zhang*, Yihua Zhang*, Shiyu Chang, Sijia Liu, and Zhangyang Wang
After read through the example, can I simply think that you are trying to train a model to addicted to one target label, so that when predicting non-target samples but added with this noise, the poisoned model will output the target label to achieve backdoor attacks?