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, 2023, and 2024)

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)

[ICLR 2020] Code for paper "Robustness Verification for Transformers"

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]

This github repository contains the official code for the paper, "Evolving Robust Neural Architectures to Defend from Adversarial Attacks"

DPLL(T)-based Verification tool for DNNs

certifying robustness of neural network via convex optimization

[NeurIPS 2021] Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | ⛰️⚠️

Sampling-based Scalable Quantitative Verification for DNNs

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"

Benchmark for formally verifying ViTs

Characterizing Data Point Vulnerability via Average-Case Robustness, UAI 2024