[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
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Jun 15, 2019 - C++
[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
Reference implementations for RecurJac, CROWN, FastLin and FastLip (Neural Network verification and robustness certification algorithms) [Do not use this repo, use https://github.com/Verified-Intelligence/auto_LiRPA instead]
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms [NeurIPS 2020]
Sampling-based Scalable Quantitative Verification for DNNs
Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).
β-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Verification
[NeurIPS 2021] Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | ⛰️
[ICLR 2020] Code for paper "Robustness Verification for Transformers"
The official repo for GCP-CROWN paper
certifying robustness of neural network via convex optimization
This code base is intended to serve as a starting point for interested researchers or practitioners to extend or apply the robustness verification portion of the author's Master's thesis " GUM-compliant neural-network robustness verification".
[CCS 2021] TSS: Transformation-specific smoothing for robustness certification
This github repository contains the official code for the papers, "Robustness Assessment for Adversarial Machine Learning: Problems, Solutions and a Survey of Current Neural Networks and Defenses" and "One Pixel Attack for Fooling Deep Neural Networks"
This github repository contains the official code for the paper, "Evolving Robust Neural Architectures to Defend from Adversarial Attacks"
An algorithm to calculate the convex hull of ReLU function for neural network verification.
auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs
Characterizing Data Point Vulnerability via Average-Case Robustness, UAI 2024
Benchmark for formally verifying ViTs
alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)
DPLL(T)-based Verification tool for DNNs
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