research-article

Generating Transferable Adversarial Examples against Vision Transformers

Authors:

Xianglong LiuAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 5181 - 5190

https://doi.org/10.1145/3503161.3547989

Published: 10 October 2022 Publication History

Abstract

Vision transformers (ViTs) are prevailing among several visual recognition tasks, therefore drawing intensive interest in generating adversarial examples against them. Different from CNNs, ViTs enjoy unique architectures, e.g., self-attention and image-embedding, which are commonly-shared features among various types of transformer-based models. However, existing adversarial methods suffer from weak transferable attacking ability due to the overlook of these architectural features. To address the problem, we propose an Architecture-oriented Transferable Attacking (ATA) framework to generate transferable adversarial examples by activating the uncertain attention and perturbing the sensitive embedding.Specifically, we first locate the patch-wise attentional regions that mostly affect model perception, therefore intensively activating the uncertainty of the attention mechanism and confusing the model decisions in turn.Furthermore, we search the pixel-wise attacking positions that are more likely to derange the embedded tokens using sensitive embedding perturbation, which could serve as a strong transferable attacking pattern.By jointly confusing the unique yet widely-used architectural features among transformer-based models, we can activate strong attacking transferability among diverse ViTs. Extensive experiments on large-scale dataset ImageNet using various popular transformers demonstrate that our ATA outperforms other baselines by large margins (at least +15% Attack Success Rate). Our code is available at https://github.com/nlsde-safety-team/ATA

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Index Terms

Generating Transferable Adversarial Examples against Vision Transformers
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision

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cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

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Published: 10 October 2022

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MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

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https://doi.org/10.1038/s41598-025-91802-6
Dingeto HKim J(2024)Comparative Study of Adversarial Defenses: Adversarial Training and Regularization in Vision Transformers and CNNsElectronics10.3390/electronics1313253413:13(2534)Online publication date: 27-Jun-2024
https://doi.org/10.3390/electronics13132534
Tambon FKhomh FAntoniol G(2024)GIST: Generated Inputs Sets Transferability in Deep LearningACM Transactions on Software Engineering and Methodology10.1145/367245733:8(1-38)Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3672457
Zhang YXie RChen JSun XWang YCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)PIP: Detecting Adversarial Examples in Large Vision-Language Models via Attention Patterns of Irrelevant Probe QuestionsProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3685510(11175-11183)Online publication date: 28-Oct-2024
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Wang HDong KZhu ZQin HLiu AFang XWang JLiu X(2024)Transferable Multimodal Attack on Vision-Language Pre-training Models2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00102(1722-1740)Online publication date: 19-May-2024
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Costa JRoxo TProença HInácio P(2024)How Deep Learning Sees the World: A Survey on Adversarial Attacks & DefensesIEEE Access10.1109/ACCESS.2024.339511812(61113-61136)Online publication date: 2024
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