MAAG: A Multi-Attention Architecture for Generalizable Multi-Target Adversarial Attacks

Ou, Dongbo; Lu, Jintian; Hua, Cheng; Zhou, Shihui; Zeng, Ying; He, Yingsheng; Tian, Jie

doi:10.3390/app15189915

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Open AccessArticle

MAAG: A Multi-Attention Architecture for Generalizable Multi-Target Adversarial Attacks

by

Dongbo Ou

,

Jintian Lu

,

Cheng Hua

,

Shihui Zhou

,

Ying Zeng

,

Yingsheng He

and

Jie Tian

^*

School of Computer Science and Engineering, Jishou University, Jishou 416000, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2025, 15(18), 9915; https://doi.org/10.3390/app15189915

Submission received: 20 August 2025 / Revised: 6 September 2025 / Accepted: 8 September 2025 / Published: 10 September 2025

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Abstract

Adversarial examples pose a severe threat to deep neural networks. They are crafted by applying imperceptible perturbations to benign inputs, causing the model to produce incorrect predictions. Most existing attack methods exhibit limited generalization, especially in black-box settings involving unseen models or unknown classes. To address these limitations, we propose MAAG (multi-attention adversarial generation), a novel model architecture that enhances attack generalizability and transferability. MAAG integrates channel and spatial attention to extract representative features for adversarial example generation and capture diverse decision boundaries for better transferability. A composite loss guides the generation of adversarial examples across different victim models. Extensive experiments validate the superiority of our proposed method in crafting adversarial examples for both known and unknown classes. Specifically, it surpasses existing generative methods by approximately 7.0% and 7.8% in attack success rate on known and unknown classes, respectively.

Keywords: adversarial attacks; multiple attention; generalization capability; multi-target attacks

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MDPI and ACS Style

Ou, D.; Lu, J.; Hua, C.; Zhou, S.; Zeng, Y.; He, Y.; Tian, J. MAAG: A Multi-Attention Architecture for Generalizable Multi-Target Adversarial Attacks. Appl. Sci. 2025, 15, 9915. https://doi.org/10.3390/app15189915

AMA Style

Ou D, Lu J, Hua C, Zhou S, Zeng Y, He Y, Tian J. MAAG: A Multi-Attention Architecture for Generalizable Multi-Target Adversarial Attacks. Applied Sciences. 2025; 15(18):9915. https://doi.org/10.3390/app15189915

Chicago/Turabian Style

Ou, Dongbo, Jintian Lu, Cheng Hua, Shihui Zhou, Ying Zeng, Yingsheng He, and Jie Tian. 2025. "MAAG: A Multi-Attention Architecture for Generalizable Multi-Target Adversarial Attacks" Applied Sciences 15, no. 18: 9915. https://doi.org/10.3390/app15189915

APA Style

Ou, D., Lu, J., Hua, C., Zhou, S., Zeng, Y., He, Y., & Tian, J. (2025). MAAG: A Multi-Attention Architecture for Generalizable Multi-Target Adversarial Attacks. Applied Sciences, 15(18), 9915. https://doi.org/10.3390/app15189915

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MAAG: A Multi-Attention Architecture for Generalizable Multi-Target Adversarial Attacks

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