RAID is the largest and most challenging benchmark for machine-generated text detectors. (ACL 2024)

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

[ICML 2024] Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models

AIShield Watchtower: Dive Deep into AI's Secrets! 🔍 Open-source tool by AIShield for AI model insights & vulnerability scans. Secure your AI supply chain today! ⚙️🛡️

A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks [IJCV2023]

A reading list for large models safety, security, and privacy.

Official Source Code of the paper "Exploring Effective Data for Surrogate Training Towards Black-box Attack", which is accepted by CVPR 2022

Quadratic Upper Bound Loss for Robust Adversarial Training

a collection of small machine learning projects

Adversary Emulation Framework

The Security Automation Toolkit

Official implementation of Black-box Universal Adversarial Attacks with Bayesian Optimization.

PAL: Proxy-Guided Black-Box Attack on Large Language Models

This project evaluates the robustness of image classification models against adversarial attacks using two key metrics: Adversarial Distance and CLEVER. The study employs variants of the WideResNet model, including a standard and a corruption-trained robust model, trained on the CIFAR-10 dataset. Key insights reveal that the CLEVER Score serves as

FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids

A list of recent papers about adversarial learning

Generate adversarial patches against YOLOv5 🚀

PyTorch implementation of adversarial attacks [torchattacks].

DPLL(T)-based Verification tool for DNNs

🎯 Enhanced Adversarial Patch: Extends adversarial attacks on TartanVO model with a new loss function, rotation attack, and momentum optimizer