OpenAttack: An Open-source Textual Adversarial Attack Toolkit
- URL: http://arxiv.org/abs/2009.09191v2
- Date: Fri, 24 Sep 2021 08:29:26 GMT
- Title: OpenAttack: An Open-source Textual Adversarial Attack Toolkit
- Authors: Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Zixian Ma, Bairu
Hou, Yuan Zang, Zhiyuan Liu, Maosong Sun
- Abstract summary: We present an open-source textual adversarial attack toolkit named OpenAttack to solve these issues.
OpenAttack has its unique strengths in support for all attack types, multilinguality, and parallel processing.
- Score: 73.22185718706602
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Textual adversarial attacking has received wide and increasing attention in
recent years. Various attack models have been proposed, which are enormously
distinct and implemented with different programming frameworks and settings.
These facts hinder quick utilization and fair comparison of attack models. In
this paper, we present an open-source textual adversarial attack toolkit named
OpenAttack to solve these issues. Compared with existing other textual
adversarial attack toolkits, OpenAttack has its unique strengths in support for
all attack types, multilinguality, and parallel processing. Currently,
OpenAttack includes 15 typical attack models that cover all attack types. Its
highly inclusive modular design not only supports quick utilization of existing
attack models, but also enables great flexibility and extensibility. OpenAttack
has broad uses including comparing and evaluating attack models, measuring
robustness of a model, assisting in developing new attack models, and
adversarial training. Source code and documentation can be obtained at
https://github.com/thunlp/OpenAttack.
Related papers
- BB-Patch: BlackBox Adversarial Patch-Attack using Zeroth-Order Optimization [10.769992215544358]
Adversarial attack strategies assume that the adversary has access to the training data, the model parameters, and the input during deployment.
We propose an black-box adversarial attack strategy that produces adversarial patches which can be applied anywhere in the input image to perform an adversarial attack.
arXiv Detail & Related papers (2024-05-09T18:42:26Z) - Are aligned neural networks adversarially aligned? [93.91072860401856]
adversarial users can construct inputs which circumvent attempts at alignment.
We show that existing NLP-based optimization attacks are insufficiently powerful to reliably attack aligned text models.
We conjecture that improved NLP attacks may demonstrate this same level of adversarial control over text-only models.
arXiv Detail & Related papers (2023-06-26T17:18:44Z) - Two-in-One: A Model Hijacking Attack Against Text Generation Models [19.826236952700256]
We propose a new model hijacking attack, Ditto, that can hijack different text classification tasks into multiple generation ones.
Our results show that by using Ditto, an adversary can successfully hijack text generation models without jeopardizing their utility.
arXiv Detail & Related papers (2023-05-12T12:13:27Z) - MultiRobustBench: Benchmarking Robustness Against Multiple Attacks [86.70417016955459]
We present the first unified framework for considering multiple attacks against machine learning (ML) models.
Our framework is able to model different levels of learner's knowledge about the test-time adversary.
We evaluate the performance of 16 defended models for robustness against a set of 9 different attack types.
arXiv Detail & Related papers (2023-02-21T20:26:39Z) - Generative Dynamic Patch Attack [6.1863763890100065]
We propose an end-to-end patch attack algorithm, Generative Dynamic Patch Attack (GDPA)
GDPA generates both patch pattern and patch location adversarially for each input image.
Experiments on VGGFace, Traffic Sign and ImageNet show that GDPA achieves higher attack success rates than state-of-the-art patch attacks.
arXiv Detail & Related papers (2021-11-08T04:15:34Z) - Composite Adversarial Attacks [57.293211764569996]
Adversarial attack is a technique for deceiving Machine Learning (ML) models.
In this paper, a new procedure called Composite Adrial Attack (CAA) is proposed for automatically searching the best combination of attack algorithms.
CAA beats 10 top attackers on 11 diverse defenses with less elapsed time.
arXiv Detail & Related papers (2020-12-10T03:21:16Z) - Learning to Attack: Towards Textual Adversarial Attacking in Real-world
Situations [81.82518920087175]
Adversarial attacking aims to fool deep neural networks with adversarial examples.
We propose a reinforcement learning based attack model, which can learn from attack history and launch attacks more efficiently.
arXiv Detail & Related papers (2020-09-19T09:12:24Z) - Adversarial Imitation Attack [63.76805962712481]
A practical adversarial attack should require as little as possible knowledge of attacked models.
Current substitute attacks need pre-trained models to generate adversarial examples.
In this study, we propose a novel adversarial imitation attack.
arXiv Detail & Related papers (2020-03-28T10:02:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.