Simple pytorch implementation of FGSM and I-FGSM

A curated list of MLSecOps tools, articles and other resources on security applied to Machine Learning and MLOps systems.

Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications

pretrained BERT model for cyber security text, learned CyberSecurity Knowledge

CTF challenges designed and implemented in machine learning applications

Train AI (Keras + Tensorflow) to defend apps with Django REST Framework + Celery + Swagger + JWT - deploys to Kubernetes and OpenShift Container Platform

Adversarial Machine Learning (AML) Capture the Flag (CTF)

Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs (ACM CCS'21)

Do you want to learn AI Security but don't know where to start ? Take a look at this map.

Adversarial perturbation intensity strategy achieving chosen intra-technique transferability level for logistic regression

Awesome-DL-Security-and-Privacy-Papers

This is the repository for Homeworks of COMP 530 Data Privacy and Security course given by Emre Gursoy at Koc University.

This repository contains the code for our ACM CCS'22 paper "StolenEncoder: Stealing Pre-Trained Encoders in Self-Supervised Learning"

Final Year Thesis Project (COMP4981H) for Computer Science Students in HKUST

Datasets for training deep neural networks to defend software applications

Summary of the presentation on Real and Stealthy Attacks on State-of-the-Art Face Recognition Systems at the Seminar: Machine Learning in Cyber-security at FU Berlin

A stochastic input pre-processing technique based on a process of down-sampling/up-sampling using convolution and transposed convolution layers. Defending convolutional neural network against adversarial attacks.