Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequence of words added to any input instance . Despite being highly successful, the word sequences produced in these attacks are often unnatural, do not carry much semantic meaning, and can be easily distinguished from natural text. In this paper, we develop adversarial attacks that appear closer to natural English phrases and yet confuse classification systems when added to benign inputs. To achieve this, we leverage an adversarially regularized autoencoder (ARAE) to generate triggers and propose a gradient-based search method to output natural text that fools a target classifier. Experiments on two different classification tasks demonstrate the effectiveness of our attacks while also being less identifiable than previous approaches on three simple detection metrics .
Recent work has demonstrated the vulnerability of modern text classifiers to universal adversarial attacks, which are input-agnostic sequences of words added to text processed by classifiers . Despite being successful, the word sequences produced in such attacks are often ungrammatical and can be easily distinguished from natural text. We develop adversarial attacks that appear closer to natural English phrases and yet confuse classification systems when added to benign inputs. We leverage an adversarially regularized autoencoder (ARAE) to generate triggers and propose a gradient-based search that aims to maximize the downstream classifier's prediction loss. Our attacks effectively reduce model accuracy on classification tasks while being less identifiable than prior models as per automatic detection metrics and human-subject studies. Our aim is to demonstrate that adversarial attacks can be made harder to detect than previously thought and to enable the development of appropriate defenses .
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sequence
sequences
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any input instance
text processed by classifiers
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highly
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unnatural, do not carry much semantic meaning,
ungrammatical
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In this paper, we
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To achieve this, we
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method to output natural text that fools a target classifier. Experiments on two different classification tasks demonstrate the effectiveness of our attacks while also
that aims to maximize the downstream classifier's prediction loss. Our attacks effectively reduce model accuracy on classification tasks while
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previous approaches on three simple detection metrics
prior models as per automatic detection metrics and human-subject studies. Our aim is to demonstrate that adversarial attacks can be made harder to detect than previously thought and to enable the development of appropriate defenses