Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
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Updated
May 10, 2024 - C++
Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)
[NeurIPS 2020] Code for "An Efficient Adversarial Attack for Tree Ensembles"
[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
Training robust models takes a looong time since generating adversaries is so expensive. We design a parallel algorithm for training large robust models using PyTorch C++/MPI and show it runs fast!
Adversarial Simulator allows one to simulate adversarial attacks in the virtual environment within the Unreal Engine and inference with Nvidia DRIVE SDK. This repository contains the code, assets, example datasets, models, test results, and a video demo for the paper :
Python API for generating adapted and unique neighbourhoods for searching for adversarial examples.
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