@inproceedings{yu-etal-2023-sparse,
title = "Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation",
author = "Yu, Zhen and
Qin, Zhou and
Chen, Zhenhua and
Lian, Meihui and
Fu, Haojun and
Wen, Weigao and
Xue, Hui and
He, Kun",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.384",
doi = "10.18653/v1/2023.findings-emnlp.384",
pages = "5775--5784",
abstract = "Deep neural networks have been widely applied in real-world scenarios, such as product restrictions on e-commerce and hate speech monitoring on social media, to ensure secure governance of various platforms. However, illegal merchants often deceive the detection models by adding large-scale perturbations to prohibited products, so as to earn illegal profits. Current adversarial attacks using imperceptible perturbations encounter challenges in simulating such adversarial behavior and evaluating the vulnerabilities of detection models to such perturbations. To address this issue, we propose a novel black-box multimodal attack, termed Sparse Multimodal Attack (SparseMA), which leverages sparse perturbations to simulate the adversarial behavior exhibited by illegal merchants in the black-box scenario. Moreover, SparseMA bridges the gap between images and texts by treating the separated image patches and text words uniformly in the discrete space. Extensive experiments demonstrate that SparseMA can identify the vulnerability of the model to different modalities, outperforming existing multimodal attacks and unimodal attacks. SparseMA, which is the first proposed method for black-box multimodal attacks to our knowledge, would be used as an effective tool for evaluating the robustness of multimodal models to different modalities.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yu-etal-2023-sparse">
<titleInfo>
<title>Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Zhen</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhou</namePart>
<namePart type="family">Qin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhenhua</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Meihui</namePart>
<namePart type="family">Lian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Haojun</namePart>
<namePart type="family">Fu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Weigao</namePart>
<namePart type="family">Wen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hui</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kun</namePart>
<namePart type="family">He</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-12</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
</titleInfo>
<name type="personal">
<namePart type="given">Houda</namePart>
<namePart type="family">Bouamor</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Juan</namePart>
<namePart type="family">Pino</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kalika</namePart>
<namePart type="family">Bali</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Singapore</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Deep neural networks have been widely applied in real-world scenarios, such as product restrictions on e-commerce and hate speech monitoring on social media, to ensure secure governance of various platforms. However, illegal merchants often deceive the detection models by adding large-scale perturbations to prohibited products, so as to earn illegal profits. Current adversarial attacks using imperceptible perturbations encounter challenges in simulating such adversarial behavior and evaluating the vulnerabilities of detection models to such perturbations. To address this issue, we propose a novel black-box multimodal attack, termed Sparse Multimodal Attack (SparseMA), which leverages sparse perturbations to simulate the adversarial behavior exhibited by illegal merchants in the black-box scenario. Moreover, SparseMA bridges the gap between images and texts by treating the separated image patches and text words uniformly in the discrete space. Extensive experiments demonstrate that SparseMA can identify the vulnerability of the model to different modalities, outperforming existing multimodal attacks and unimodal attacks. SparseMA, which is the first proposed method for black-box multimodal attacks to our knowledge, would be used as an effective tool for evaluating the robustness of multimodal models to different modalities.</abstract>
<identifier type="citekey">yu-etal-2023-sparse</identifier>
<identifier type="doi">10.18653/v1/2023.findings-emnlp.384</identifier>
<location>
<url>https://aclanthology.org/2023.findings-emnlp.384</url>
</location>
<part>
<date>2023-12</date>
<extent unit="page">
<start>5775</start>
<end>5784</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation
%A Yu, Zhen
%A Qin, Zhou
%A Chen, Zhenhua
%A Lian, Meihui
%A Fu, Haojun
%A Wen, Weigao
%A Xue, Hui
%A He, Kun
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yu-etal-2023-sparse
%X Deep neural networks have been widely applied in real-world scenarios, such as product restrictions on e-commerce and hate speech monitoring on social media, to ensure secure governance of various platforms. However, illegal merchants often deceive the detection models by adding large-scale perturbations to prohibited products, so as to earn illegal profits. Current adversarial attacks using imperceptible perturbations encounter challenges in simulating such adversarial behavior and evaluating the vulnerabilities of detection models to such perturbations. To address this issue, we propose a novel black-box multimodal attack, termed Sparse Multimodal Attack (SparseMA), which leverages sparse perturbations to simulate the adversarial behavior exhibited by illegal merchants in the black-box scenario. Moreover, SparseMA bridges the gap between images and texts by treating the separated image patches and text words uniformly in the discrete space. Extensive experiments demonstrate that SparseMA can identify the vulnerability of the model to different modalities, outperforming existing multimodal attacks and unimodal attacks. SparseMA, which is the first proposed method for black-box multimodal attacks to our knowledge, would be used as an effective tool for evaluating the robustness of multimodal models to different modalities.
%R 10.18653/v1/2023.findings-emnlp.384
%U https://aclanthology.org/2023.findings-emnlp.384
%U https://doi.org/10.18653/v1/2023.findings-emnlp.384
%P 5775-5784
Markdown (Informal)
[Sparse Black-Box Multimodal Attack for Vision-Language Adversary Generation](https://aclanthology.org/2023.findings-emnlp.384) (Yu et al., Findings 2023)
ACL