auto-attack VS KitanaQA

Compare auto-attack vs KitanaQA and see what are their differences.

auto-attack

Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks" (by fra31)
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auto-attack KitanaQA
3 1
607 57
- -
0.0 0.0
4 months ago 10 months ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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auto-attack

Posts with mentions or reviews of auto-attack. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-21.
  • DARPA Open Sources Resources to Aid Evaluation of Adversarial AI Defenses
    2 projects | news.ycombinator.com | 21 Dec 2021
    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

  • [D] Testing a model's robustness to adversarial attacks
    2 projects | /r/MachineLearning | 30 Jan 2021
    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.

KitanaQA

Posts with mentions or reviews of KitanaQA. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-01.
  • Ask HN: Who is hiring? (February 2021)
    16 projects | news.ycombinator.com | 1 Feb 2021
    Searchable.ai | Full Stack Engineer | Full Time | Remote U.S.

    We help our users find their stuff wherever it's stored as we build the future of enterprise search.

    We are hiring a full-stack engineer with Rails experience to join our growing engineering team. Our stack includes: Rails, Electron, Webpack, PostgreSQL, Elasticsearch & Kubernetes.

    This position will also have the opportunity to help integrate our research teams' SOTA work into our product to help users ask questions across their files (see: https://github.com/searchableai/kitanaqa).

    Full description here: https://www.searchable.ai/full-stack-engineer/ and drop us a line at careers at searchable dot ai if you're interested!

What are some alternatives?

When comparing auto-attack and KitanaQA you can also consider the following projects:

adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

FinBERT-QA - Financial Domain Question Answering with pre-trained BERT Language Model

DeepRobust - A pytorch adversarial library for attack and defense methods on images and graphs

akvo-flow - A data collection and monitoring tool that works anywhere.

alpha-beta-CROWN - alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)

ozone - Scalable, redundant, and distributed object store for Apache Hadoop

eClaire - Trello card printer

OpenAttack - An Open-Source Package for Textual Adversarial Attack.

inltk - Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need

bertviz - BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)