There are 2 repositories under robustness-verification topic.
auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs
alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)
Neural Network Verification Software Tool
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)
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]
[ICLR 2020] Code for paper "Robustness Verification for Transformers"
[NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contribution)
[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
Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms [NeurIPS 2020]
[NeurIPS 2021] Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | ⛰️⚠️
An algorithm to calculate the convex hull of ReLU function for neural network verification.
Benchmark for formally verifying ViTs
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 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".