Benchmark for LP-relaxed robustness verification of ReLU-networks
☆42Apr 24, 2019Updated 7 years ago
Alternatives and similar repositories for robust-verify-benchmark
Users that are interested in robust-verify-benchmark are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Efficient Robustness Verification for ReLU networks (this repository is outdated, don't use; checkout our new implementation at https://g…☆30Nov 1, 2019Updated 6 years ago
- Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"☆229Nov 9, 2019Updated 6 years ago
- Certified defense to adversarial examples using CROWN and IBP. Also includes GPU implementation of CROWN verification algorithm (in PyTor…☆99Jun 7, 2021Updated 5 years ago
- Code for Stability Training with Noise (STN)☆22Dec 27, 2020Updated 5 years ago
- "Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers" (NeurIPS 2019, previously called "A Stratified Approach …☆17Nov 16, 2019Updated 6 years ago
- Proton VPN Special Offer - Get 70% off • AdSpecial partner offer. Trusted by over 100 million users worldwide. Tested, Approved and Recommended by Experts.
- Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs☆102Apr 2, 2021Updated 5 years ago
- CROWN: A Neural Network Robustness Certification Algorithm for General Activation Functions (This repository is outdated; use https://git…☆18Nov 29, 2018Updated 7 years ago
- Code for paper "Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers"☆17Jan 27, 2023Updated 3 years ago
- ☆15Dec 7, 2021Updated 4 years ago
- auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs☆358Updated this week
- ☆35Aug 30, 2021Updated 5 years ago
- An Algorithm to Quantify Robustness of Recurrent Neural Networks☆49Apr 24, 2020Updated 6 years ago
- Official implementation for Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds (NeurIPS, 2021).☆25Sep 4, 2022Updated 4 years ago
- Code of On L-p Robustness of Decision Stumps and Trees, ICML 2020☆10Aug 3, 2020Updated 6 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- Reachability Analysis of Deep Neural Networks with Provable Guarantees☆36Feb 25, 2020Updated 6 years ago
- Robustness for Non-Parametric Classification: A Generic Attack and Defense☆18Nov 21, 2022Updated 3 years ago
- A method for training neural networks that are provably robust to adversarial attacks. [IJCAI 2019]☆10Sep 3, 2019Updated 7 years ago
- A certifiable defense against adversarial examples by training neural networks to be provably robust☆221Jul 25, 2024Updated 2 years ago
- A community-run reference for state-of-the-art adversarial example defenses.☆52Oct 13, 2024Updated last year
- Code for the paper "(De)Randomized Smoothing for Certifiable Defense against Patch Attacks" by Alexander Levine and Soheil Feizi.☆17Aug 22, 2022Updated 4 years ago
- Geometric Certifications of Neural Nets☆42Nov 22, 2022Updated 3 years ago
- Semisupervised learning for adversarial robustness https://arxiv.org/pdf/1905.13736.pdf☆140Mar 30, 2020Updated 6 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Mar 1, 2022Updated 4 years ago
- Managed Database hosting by DigitalOcean • AdPostgreSQL, MySQL, MongoDB, Kafka, Valkey, and OpenSearch available. Automatically scale up storage and focus on building your apps.
- Public code for a paper "Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks."☆35Dec 18, 2018Updated 7 years ago
- β-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Verification☆31Nov 9, 2021Updated 4 years ago
- The Charon tool for analyzing neural network robustness☆13Mar 19, 2020Updated 6 years ago
- [NeurIPS 2019] H. Chen*, H. Zhang*, S. Si, Y. Li, D. Boning and C.-J. Hsieh, Robustness Verification of Tree-based Models (*equal contrib…☆27Jun 15, 2019Updated 7 years ago
- Code for CVPR2020 paper QEBA: Query-Efficient Boundary-Based Blackbox Attack☆32Feb 21, 2021Updated 5 years ago
- AAAI 2019 oral presentation☆53Jul 17, 2026Updated 2 months ago
- Fastened CROWN: Tightened Neural Network Robustness Certificates☆10Feb 10, 2020Updated 6 years ago
- OVAL framework for BaB-based Neural Network Verification☆18May 8, 2026Updated 5 months ago
- Notebooks for reproducing the paper "Computer Vision with a Single (Robust) Classifier"☆129Oct 24, 2019Updated 6 years ago
- Bare Metal GPUs on DigitalOcean Gradient AI • AdPurpose-built for serious AI teams training foundational models, running large-scale inference, and pushing the boundaries of what's possible.
- Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks [NeurIPS 2019]☆50Apr 25, 2020Updated 6 years ago
- to add☆20Nov 21, 2019Updated 6 years ago
- code for model-targeted poisoning☆12Oct 3, 2023Updated 3 years ago
- ETH Robustness Analyzer for Deep Neural Networks☆347Jan 27, 2023Updated 3 years ago
- IPython notebook with synthetic experiments for AFLite, based on the ICML 2020 paper, "Adversarial Filters of Dataset Biases".☆16Aug 14, 2020Updated 6 years ago
- Repository for Certified Defenses for Adversarial Patch ICLR-2020☆34Sep 18, 2020Updated 6 years ago
- A method for training neural networks that are provably robust to adversarial attacks.☆392Feb 16, 2022Updated 4 years ago