athena
plexiglass
| athena | plexiglass | |
|---|---|---|
| 1 | 3 | |
| 42 | 101 | |
| - | - | |
| 0.0 | 9.0 | |
| over 2 years ago | 4 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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athena
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How to Put Out Democracy’s Dumpster Fire: Our democratic habits have been killed off by an internet kleptocracy that profits from disinformation, polarization, and rage. Here’s how to fix that.
While users could bookmark algorithms for use anywhere on reddit, the default sorting mode for a subreddit would be established by an ensemble of the algorithms, weighted by the usage of the those algorithms on that subreddit. Such a system could be robust against bot attacks, as an adversary must defeat not one algorithm, but the majority of algorithms used (see Athena: "A Framework for Defending Machine Learning Systems Against Adversarial Attacks").
plexiglass
- Looking for contributors to an AI security project
- [P] Plexiglass: a toolbox for testing against adversarial attacks in DNNs and LLMs.
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Plexiglass: A toolbox for testing against adversarial attacks in DNNs and LLMs
Hi everyone, my name is Enoch and I am a researcher studying deep generative models.
I've started this project called Plexiglass a while back, which started off as a torch toolbox for adversarial research in DCNNs. I am now rebooting it as a toolbox for testing against adversarial attacks in both DNNs and LLMs.
Idea is to test your DCNNs against adversarial attacks such as fast gradient sign method and toxic prompts in LLMs.
I would very much appreciate contributions, I need more devs as I'm too busy to do this all by myself .
Repo is here: https://github.com/kortex-labs/plexiglass
What are some alternatives?
fawkes - Fawkes, privacy preserving tool against facial recognition systems. More info at https://sandlab.cs.uchicago.edu/fawkes
docs - TensorFlow documentation
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HackThisAI - Adversarial Machine Learning (AML) Capture the Flag (CTF)
faceswap - Deepfakes Software For All
notebooks - Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.