DeepRobust VS auto-attack

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

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

Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks" (by fra31)
adversarial-defenses adversarial-attacks adversarial-robustness
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DeepRobust auto-attack
1 3
940 607
- -
5.5 0.0
5 days ago 3 months ago
Python Python
MIT License MIT License
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DeepRobust

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

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.

What are some alternatives?

When comparing DeepRobust and auto-attack 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

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

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

ccat - Cisco Config Analysis Tool

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

text_gcn - Graph Convolutional Networks for Text Classification. AAAI 2019

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

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