auto_LiRPA
adversarial-robustness-toolbox
auto_LiRPA | adversarial-robustness-toolbox | |
---|---|---|
1 | 8 | |
266 | 4,508 | |
2.6% | 2.3% | |
4.2 | 9.7 | |
about 1 month ago | 5 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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auto_LiRPA
adversarial-robustness-toolbox
- [D] Couldn't devs of major GPTs have added an invisible but detectable watermark in the models?
- [D] ML Researchers/Engineers in Industry: Why don't companies use open source models more often?
- [D]: How safe is it to just use a strangers Model?
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[D] Does anyone care about adversarial attacks anymore?
Check out this project https://github.com/Trusted-AI/adversarial-robustness-toolbox
- adversarial-robustness-toolbox: Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
- Library for Machine Learning Security Evasion, Poisoning, Extraction, Inference
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Introduction to Adversarial Machine Learning
Adversarial Robustness Toolbox (ART) is a Python library for Machine Learning Security. ART provides tools that enable developers and researchers to defend and evaluate Machine Learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference.
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[D] Testing a model's robustness to adversarial attacks
Depending on what attacks you want I've found both https://github.com/cleverhans-lab/cleverhans and https://github.com/Trusted-AI/adversarial-robustness-toolbox to be useful.
What are some alternatives?
mlattacks - Machine Learning Attack Series
DeepRobust - A pytorch adversarial library for attack and defense methods on images and graphs
mn-bab - [ICLR 2022] Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound
auto-attack - Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
alpha-beta-CROWN - alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)
alpha-zero-boosted - A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)
m2cgen - Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
waf-bypass - Check your WAF before an attacker does
Differential-Privacy-Guide - Differential Privacy Guide
gretel-synthetics - Synthetic data generators for structured and unstructured text, featuring differentially private learning.