Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination

Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination

Abstract Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model and its output information, transfer...

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Bibliographic Details
Main Authors: Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang
Format: Article
Language:English
Published: Springer 2025-01-01
Series:Complex & Intelligent Systems
Subjects:
Deep learning security
Pattern recognition
Image classification
Adversarial example
Adversarial defense
Online Access:https://doi.org/10.1007/s40747-024-01770-z
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https://doi.org/10.1007/s40747-024-01770-z

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