Targeted universal adversarial examples for remote sensing

Researchers are focusing on the vulnerabilities of deep learning models for remote sensing; various attack methods have been proposed, including universal adversarial examples. Existing universal adversarial examples, however, are only designed to fool deep learning models rather than target specifi...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: Bai, Tao, Wang, Hao, Wen, Bihan
مؤلفون آخرون: School of Electrical and Electronic Engineering
التنسيق: مقال
اللغة:English
منشور في: 2023
الموضوعات:
الوصول للمادة أونلاين:https://hdl.handle.net/10356/165409
الوسوم: إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
الوصف
الملخص:Researchers are focusing on the vulnerabilities of deep learning models for remote sensing; various attack methods have been proposed, including universal adversarial examples. Existing universal adversarial examples, however, are only designed to fool deep learning models rather than target specific goals, i.e., targeted attacks. To this end, we propose two variants of universal adversarial examples called targeted universal adversarial examples and source-targeted universal adversarial examples. Extensive experiments on three popular datasets showed strong attackability of the two targeted adversarial variants. We hope such strong attacks can inspire and motivate research on the defenses against adversarial examples in remote sensing.