adversarial-robustness-toolbox
auto-attack
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| adversarial-robustness-toolbox | auto-attack | |
|---|---|---|
| 8 | 3 | |
| 4,447 | 607 | |
| 2.6% | - | |
| 9.7 | 0.0 | |
| 6 days ago | 3 months ago | |
| Python | Python | |
| MIT License | MIT License |
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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.
auto-attack
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DARPA Open Sources Resources to Aid Evaluation of Adversarial AI Defenses
I'm less familiar with poisoning, but at least for test-time robustness, the current benchmark for image classifiers is AutoAttack [0,1]. It's an ensemble of adaptive & parameter-free gradient-based and black-box attacks. Submitted academic work is typically considered incomplete without an evaluation on AA (and sometimes deepfool [2]). It is good to see that both are included in ART.
[0] https://arxiv.org/abs/2003.01690
[1] https://github.com/fra31/auto-attack
[2] https://arxiv.org/abs/1511.04599
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[D] Testing a model's robustness to adversarial attacks
A better method is to use the AutoAttack from Croce et al. https://github.com/fra31/auto-attack which is much more robust to gradient masking. It's actually a combination of 3 attacks (2 white-box and 1 black box) with good default hyper-parameters. It's not perfect but it gives a more accurate robustness.
What are some alternatives?
DeepRobust - A pytorch adversarial library for attack and defense methods on images and graphs
TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
alpha-zero-boosted - A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)
KitanaQA - KitanaQA: Adversarial training and data augmentation for neural question-answering models
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
alpha-beta-CROWN - alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)
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.
unrpa - A program to extract files from the RPA archive format.