auto-attack VS DeepRobust

Compare auto-attack vs DeepRobust 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)
adversarial-defenses adversarial-attacks adversarial-robustness
Source Code
arxiv.org
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DeepRobust

A pytorch adversarial library for attack and defense methods on images and graphs (by DSE-MSU)
adversarial-attacks adversarial-examples deep-neural-networks defense graph-neural-networks Machine Learning Deep Learning graph-convolutional-networks graph-mining
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auto-attack DeepRobust
3 1
608 942
- -
0.0 5.5
4 months ago 10 days ago
Python Python
MIT License MIT License
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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.

DeepRobust

Posts with mentions or reviews of DeepRobust. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing auto-attack and DeepRobust 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/

torchdrug - A powerful and flexible machine learning platform for drug discovery

KitanaQA - KitanaQA: Adversarial training and data augmentation for neural question-answering models

ccat - Cisco Config Analysis Tool

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

text_gcn - Graph Convolutional Networks for Text Classification. AAAI 2019

auto-attack vs adversarial-robustness-toolbox DeepRobust vs adversarial-robustness-toolbox auto-attack vs TextAttack DeepRobust vs torchdrug auto-attack vs KitanaQA DeepRobust vs ccat auto-attack vs alpha-beta-CROWN DeepRobust vs text_gcn