adversarial-robustness-toolbox
gretel-synthetics
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| adversarial-robustness-toolbox | gretel-synthetics | |
|---|---|---|
| 8 | 4 | |
| 4,460 | 533 | |
| 2.9% | 4.9% | |
| 9.7 | 7.3 | |
| 6 days ago | 18 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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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.
gretel-synthetics
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Ask HN: If we train an LLM with “data” instead of “language” tokens
Hey there! Co-founder of Gretel.ai here, and I think I can provide some insights on this topic.
Firstly, the concept you're hinting at is not purely traditional ML. In traditional machine learning, we often prioritize feature extraction and engineering specific to a given problem space before training.
What you're describing and what we've been working on at Gretel.ai, is leveraging the power of models like Large Language Models (LLMs) to understand and extrapolate from vast amounts of diverse data without the need for time-consuming feature engineering. Here's a link to our open-source library https://github.com/gretelai/gretel-synthetics for synthetic data generation (currently supporting GAN and RNN-based language models), and also our recent announcement around a Tabular LLM we're training to help people build with data https://gretel.ai/tabular-llm
A few areas where we've found tabular or Large Data Models to be really useful are:
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Libraries for synthetic data?
you can try QuantGAN: https://github.com/PakAndrey/QuantGANforRisk also try DoppelGANger https://github.com/gretelai/gretel-synthetics/tree/master/src/gretel_synthetics/timeseries_dgan
- Which open source tool for generating synthetic data sets?
- Gretel-synthetics: open-source library to create synthetic datasets
What are some alternatives?
DeepRobust - A pytorch adversarial library for attack and defense methods on images and graphs
Copulas - A library to model multivariate data using copulas.
auto-attack - Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.
TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/
rex-gym - OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
alpha-zero-boosted - A "build to learn" Alpha Zero implementation using Gradient Boosted Decision Trees (LightGBM)
CTGAN - Conditional GAN for generating synthetic tabular data.
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
AI-basketball-analysis - :basketball::robot::basketball: AI web app and API to analyze basketball shots and shooting pose.
waf-bypass - Check your WAF before an attacker does
RobustVideoMatting - Robust Video Matting in PyTorch, TensorFlow, TensorFlow.js, ONNX, CoreML!