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Generating Natural Adversarial Examples
[article]
2018
arXiv
pre-print
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In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing ...
We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers. ...
FRAMEWORK FOR GENERATING NATURAL ADVERSARIES In this section, we describe the problem setup and details of our framework for generating natural adversarial examples of both continuous images and discrete ...
arXiv:1710.11342v2
fatcat:urv6jnxcgfetpfloq7gjdowdtm
Generating Natural Language Adversarial Examples
[article]
2018
arXiv
pre-print
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Given these challenges, we use a black-box population-based optimization algorithm to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and ...
Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. ...
Relative to the image domain, little work has been pursued for generating natural language adversarial examples. ...
arXiv:1804.07998v2
fatcat:c5kknmwlprfwdotgnswgkdjdby
Generating Natural Language Adversarial Examples
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
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Given these challenges, we use a black-box population-based optimization algorithm to generate semantically and syntactically similar adversarial examples that fool well-trained sentiment analysis and ...
Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify. ...
Relative to the image domain, little work has been pursued for generating natural language adversarial examples. ...
doi:10.18653/v1/d18-1316
dblp:conf/emnlp/AlzantotSEHSC18
fatcat:orn7hr6hprhptpecx46q2oedqa
Generating Fluent Adversarial Examples for Natural Languages
[article]
2020
arXiv
pre-print
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Secondly, the fluency of the generated examples cannot be guaranteed. ...
Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. ...
Conclusion In this paper, we propose MHA, which generates adversarial examples for natural language by adopting the MH sampling approach. ...
arXiv:2007.06174v1
fatcat:kvl555rxpfd7np62fid3bqyuqe
Generating Fluent Adversarial Examples for Natural Languages
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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Secondly, the fluency of the generated examples cannot be guaranteed. ...
Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. ...
Conclusion In this paper, we propose MHA, which generates adversarial examples for natural language by adopting the MH sampling approach. ...
doi:10.18653/v1/p19-1559
dblp:conf/acl/ZhangZML19
fatcat:ersm2mvcqnh5hf2x5yfuft3esu
A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples
[article]
2020
arXiv
pre-print
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Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of variable lengths. ...
In this paper, we propose a geometry-inspired attack for generating natural language adversarial examples. ...
Generating adversarial examples for natural language is fundamentally different from generating adversarial examples for images. ...
arXiv:2010.01345v1
fatcat:f7mp4aqztvbsbmv6r4p32tcjwm
Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN
[article]
2020
arXiv
pre-print
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In this paper, we present a new method which is able to generate natural adversarial examples from the true data following the second paradigm. ...
Based on Generative Adversarial Networks (GANs) [5], it reweights the true data empirical distribution to encourage the classifier to generate ad-versarial examples. ...
So the adversarial examples look like true images and are adversarial for the attacked classifier. We propose a different approach to generate natural adversarial examples from those methods. ...
arXiv:2001.09993v1
fatcat:3d2qzur3vrgu3muemmxi5jltru
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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The challenge of this task is to generate adversarial examples that maintain lexical correctness, grammatical correctness and semantic similarity. ...
A human evaluation study shows that our generated adversarial examples maintain the semantic similarity well and are hard for humans to perceive. ...
In the following, we first give a definition of adversarial example for natural language classification, and then introduce our word substitution strategy. ...
doi:10.18653/v1/p19-1103
dblp:conf/acl/RenDHC19
fatcat:cgbeqhguzjh7vets2af7vjzwjm
Generating Natural Language Adversarial Examples through An Improved Beam Search Algorithm
[article]
2021
arXiv
pre-print
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Also, further experiments show the novel method has a good transferability on the generated adversarial examples. ...
However, these attack methods are inefficient as they require lots of queries for the victim model when crafting text adversarial examples. ...
adversarial examples
for tasks such as Sentiment Analysis and Natural Lan-
guage Inference.
2. ...
arXiv:2110.08036v1
fatcat:zipry7m2enfg3jlxzw3cg5kyge
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models
[article]
2020
arXiv
pre-print
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Specifically, we train a conditional variational autoencoder (VAE) with an additional adversarial loss to guide the generation of adversarial examples. ...
However, these classifiers are found to be easily fooled by adversarial examples. ...
In natural language texts, even a single word change may change the whole meaning of a sentence. A valid adversarial example must be imperceptible to humans. ...
arXiv:2003.10388v1
fatcat:tuwfajw7nzblhgeiu72zjxxogq
A Geometry-Inspired Attack for Generating Natural Language Adversarial Examples
2020
Proceedings of the 28th International Conference on Computational Linguistics
unpublished
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The file type is application/pdf.
Generating adversarial examples for natural language is hard, as natural language consists of discrete symbols, and examples are often of variable lengths. ...
In this paper, we propose a geometryinspired attack for generating natural language adversarial examples. ...
Generating adversarial examples for natural language is fundamentally different from generating adversarial examples for images. ...
doi:10.18653/v1/2020.coling-main.585
fatcat:7wpnzeeywbbaldc5qq45ank4ym
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification
2021
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
unpublished
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Our findings suggest that human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentimentpreserving examples, though they do so by being much more ...
We analyze how human-generated adversarial examples compare to the recently proposed TEXTFOOLER, GENETIC, BAE and SEMEMEPSO attack algorithms on the dimensions naturalness, preservation of sentiment, grammaticality ...
These findings show that under similar constraints, machine-generated, word-level adversarial examples are comparable to human-generated ones with respect to their naturalness and grammaticality. ...
doi:10.18653/v1/2021.emnlp-main.651
fatcat:w6x4ddxujbczjoiaxcdwmc2tlu
Feature-Based Adversarial Training for Deep Learning Models Resistant to Transferable Adversarial Examples
2022
IEICE transactions on information and systems
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Adversaries may generate adversarial examples using the property of transferability, in which adversarial examples that deceive one model can also deceive other models because adversaries do not obtain ...
We propose a novel adversarial training method to train DNNs to be robust against transferable adversarial examples and maximize their classification accuracy for natural images. ...
For example, we generate adversarial examples to deceive the model that is naturally trained by Xception and then input the generated adversarial examples into the naturally trained model as well as those ...
doi:10.1587/transinf.2021edp7198
fatcat:gu2mrqe54vb5nctck74ivaazpq
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness
[article]
2021
arXiv
pre-print
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adversarial examples, leading to a dramatic decrease in natural accuracy. ...
class-conditional feature adaption across natural data and adversarial examples. ...
(a) Shift between natural and adversarial data. Note that the adversarial examples are generated by adding imperceptible perturbations over the natural data. ...
arXiv:2112.00323v1
fatcat:qf4vdsli6jbadhruy2kan7nfau
ReabsNet: Detecting and Revising Adversarial Examples
[article]
2017
arXiv
pre-print
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Critically, instead of simply rejecting adversarial examples, we revise them to get their true labels. ...
The approach is to augment an existing classification network with a guardian network to detect if a sample is natural or has been adversarially perturbed. ...
Subsequently, we generate adversarial data for each natural examples in the training dataset through one of the attacking methods discussed in Section 3.2. ...
arXiv:1712.08250v1
fatcat:ozxpdvlkbfgkncowqot7wvtw6a
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