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

DPLL(T)-based Verification tool for DNNs

certifying robustness of neural network via convex optimization

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]

An algorithm to calculate the convex hull of ReLU function for neural network verification.

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".