Shoplifting Smart Stores Using Adversarial Machine Learning

被引：0
|
作者：
Nassar, Mohamed [1 ]
Itani, Abdallah [1 ]
Karout, Mahmoud [1 ]
El Baba, Mohamad [1 ]
Kaakaji, Omar Al Samman [1 ]
机构：
[1] Amer Univ Beirut AUB, Dept Comp Sci, Fac Arts & Sci, Beirut, Lebanon
来源：
2019 IEEE/ACS 16TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA 2019) | 2019年
关键词：
Smart Stores; Adversarial Machine Learning; Adversarial Patch; Deep Learning; Classification; Convolutional Neural Networks; Object Recognition;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Smart stores cashier-less technology is partially based on camera-equipped object detection systems. Powerful machine learning algorithms are deployed at the back-end for classification. In this paper, we explore the usage of adversarial machine learning techniques to deceive the smart stores' classifiers. In particular, we experiment with printable adversarial patches and target making an expensive item classified as a cheaper one. By sticking patches to the objects and lifting them, a customer can make her customized discounts and alter the machine learning prediction. We discuss experiments, results, and possible countermeasures.
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页数：6
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