A curated list of trustworthy deep learning papers. Daily updating...
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Updated
Jun 6, 2024
A curated list of trustworthy deep learning papers. Daily updating...
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
The Security Toolkit for LLM Interactions
Official Source Code of the paper "Exploring Effective Data for Surrogate Training Towards Black-box Attack", which is accepted by CVPR 2022
The fastest && easiest LLM security and privacy guardrails for GenAI apps.
A curated list of useful resources that cover Offensive AI.
RSS feed for adversarial example papers.
Feature Scattering Adversarial Training (NeurIPS19)
Parseval Networks and Adversarial Examples
FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids
Papers and resources related to the security and privacy of LLMs 🤖
Generate adversarial patches against YOLOv5 🚀
ChatGPT Jailbreaks, GPT Assistants Prompt Leaks, GPTs Prompt Injection, LLM Prompt Security, Super Prompts, Prompt Hack, Prompt Security, Ai Prompt Engineering, Adversarial Machine Learning.
Official implementation of "Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models"
TransferAttack is a pytorch framework to boost the adversarial transferability for image classification.
👀🛡️ Code for the paper “Carefully Blending Adversarial Training and Purification Improves Adversarial Robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2024)
Birhanu Eshete is an Associate Professor of Computer Science at the University of Michigan, Dearborn. His main research focus is in trustworthy machine learning with emphasis on security, safety, privacy, interpretability, fairness, and the dynamics thereof. He also studies online cybercrime and advanced and persistent threats (APTs).
Evaluating adversarial machine learning attacks in network intrusion detection systems.
Machine Learning Attack Series
A Python library for Secure and Explainable Machine Learning
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