<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mosca-etal-2022-suspicious">
    <titleInfo>
        <title>“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Edoardo</namePart>
        <namePart type="family">Mosca</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shreyash</namePart>
        <namePart type="family">Agarwal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Javier</namePart>
        <namePart type="family">Rando Ramírez</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Georg</namePart>
        <namePart type="family">Groh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Preslav</namePart>
            <namePart type="family">Nakov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Aline</namePart>
            <namePart type="family">Villavicencio</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Dublin, Ireland</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Adversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications. Extensive research in computer vision has been carried to develop reliable defense strategies. However, the same issue remains less explored in natural language processing. Our work presents a model-agnostic detector of adversarial text examples. The approach identifies patterns in the logits of the target classifier when perturbing the input text. The proposed detector improves the current state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks.</abstract>
    <identifier type="citekey">mosca-etal-2022-suspicious</identifier>
    <identifier type="doi">10.18653/v1/2022.acl-long.538</identifier>
    <location>
        <url>https://aclanthology.org/2022.acl-long.538</url>
    </location>
    <part>
        <date>2022-05</date>
        <extent unit="page">
            <start>7806</start>
            <end>7816</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T “That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks
%A Mosca, Edoardo
%A Agarwal, Shreyash
%A Rando Ramírez, Javier
%A Groh, Georg
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F mosca-etal-2022-suspicious
%X Adversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications. Extensive research in computer vision has been carried to develop reliable defense strategies. However, the same issue remains less explored in natural language processing. Our work presents a model-agnostic detector of adversarial text examples. The approach identifies patterns in the logits of the target classifier when perturbing the input text. The proposed detector improves the current state-of-the-art performance in recognizing adversarial inputs and exhibits strong generalization capabilities across different NLP models, datasets, and word-level attacks.
%R 10.18653/v1/2022.acl-long.538
%U https://aclanthology.org/2022.acl-long.538
%U https://doi.org/10.18653/v1/2022.acl-long.538
%P 7806-7816

Markdown (Informal)

[“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks](https://aclanthology.org/2022.acl-long.538) (Mosca et al., ACL 2022)

• “That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks (Mosca et al., ACL 2022)

ACL

• Edoardo Mosca, Shreyash Agarwal, Javier Rando Ramírez, and Georg Groh. 2022. “That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7806–7816, Dublin, Ireland. Association for Computational Linguistics.