alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, 2023, 2024, 2025)
auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs
Neural Network Verification Software Tool https://www.verivital.com Documentation:
Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTorch).
Formal Verification of Neural Feedback Loops (NFLs)
A neural network verification tool based on the DPLL(T) SMT Solving algorithm.
β-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Verification
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]
[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
[ICLR 2020] Code for paper "Robustness Verification for Transformers"
[CCS 2021] TSS: Transformation-specific smoothing for robustness certification
This github repository contains the official code for the paper, "Evolving Robust Neural Architectures to Defend from Adversarial Attacks"
The official repo for GCP-CROWN paper
certifying robustness of neural network via convex optimization
WraLU is an artifact for the paper "ReLU Hull Approximation" (POPL'24), which provides a sound but incomplete neural network verifier by over-approximating ReLU function hull.
Sampling-based Scalable Quantitative Verification for DNNs
[NeurIPS 2021] Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | ⛰️
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms [NeurIPS 2020]
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"
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