Open AccessPosted Content
Towards Crafting Text Adversarial Samples.
Suranjana Samanta,Sameep Mehta +1 more
Reads0
Chats0
TLDR
This paper proposes a new method of crafting adversarial text samples by modification of the original samples, which works best for the datasets which have sub-categories within each of the classes of examples.Abstract:
Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a classifier at hand. An attacker introduces specially crafted adversarial samples to a deployed classifier, which are being mis-classified by the classifier. However, the samples are perceived to be drawn from entirely different classes and thus it becomes hard to detect the adversarial samples. Most of the prior works have been focused on synthesizing adversarial samples in the image domain. In this paper, we propose a new method of crafting adversarial text samples by modification of the original samples. Modifications of the original text samples are done by deleting or replacing the important or salient words in the text or by introducing new words in the text sample. Our algorithm works best for the datasets which have sub-categories within each of the classes of examples. While crafting adversarial samples, one of the key constraint is to generate meaningful sentences which can at pass off as legitimate from language (English) viewpoint. Experimental results on IMDB movie review dataset for sentiment analysis and Twitter dataset for gender detection show the efficiency of our proposed method.read more
Citations
More filters
Proceedings Article
Synthetic and Natural Noise Both Break Neural Machine Translation
Yonatan Belinkov,Yonatan Bisk +1 more
TL;DR: It is found that a model based on a character convolutional neural network is able to simultaneously learn representations robust to multiple kinds of noise, including structure-invariant word representations and robust training on noisy texts.
Proceedings ArticleDOI
Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
TL;DR: DeepWordBug as mentioned in this paper generates small text perturbations in a black-box setting that force a deep-learning classifier to misclassify a text input by scoring strategies to find the most important words to modify.
Proceedings ArticleDOI
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency.
TL;DR: A new word replacement order determined by both the wordsaliency and the classification probability is introduced, and a greedy algorithm called probability weighted word saliency (PWWS) is proposed for text adversarial attack.
Journal ArticleDOI
Analysis Methods in Neural Language Processing: A Survey
Yonatan Belinkov,James Glass +1 more
TL;DR: Analysis methods in neural language processing are reviewed, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.
Journal ArticleDOI
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
TL;DR: A systematic and comprehensive overview of the main threats of attacks and the success of corresponding countermeasures against adversarial examples, for three most popular data types, including images, graphs and text is reviewed.
References
More filters
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner,Patrick Haffner +7 more
TL;DR: This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task, and Convolutional neural networks are shown to outperform all other techniques.
Posted Content
Explaining and Harnessing Adversarial Examples
TL;DR: The authors argue that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature, which is supported by new quantitative results while giving the first explanation of the most intriguing fact about adversarial examples: their generalization across architectures and training sets.
Proceedings Article
Learning Word Vectors for Sentiment Analysis
TL;DR: This work presents a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term--document information as well as rich sentiment content, and finds it out-performs several previously introduced methods for sentiment classification.