Ecgadv: Generating adversarial electrocardiogram to misguide arrhythmia classification system

Chen，Huangxun1; Huang，Chenyu1; Huang，Qianyi1,2; Zhang，Qian1; Wang，Wei3

2020

34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence

2159-5399

2374-3468

AAAI 2020 - 34th AAAI Conference on Artificial Intelligence

34

3446-3453

FEB 07-12, 2020

null,New York,NY

2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE

Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems recently achieve promising progress to take over tedious examinations by cardiologists. However, their vulnerability to adversarial attacks still lack comprehensive investigation. The existing attacks in image domain could not be directly applicable due to the distinct properties of ECGs in visualization and dynamic properties. Thus, this paper takes a step to thoroughly explore adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the properties of ECGs to design effective attacks schemes under two attacks models respectively. Our results demonstrate the blind spots of DNN-powered diagnosis systems under adversarial attacks, which calls attention to adequate countermeasures.

其他

英语

CPCI-S ; EI

RGC["CERG 16203719",16204418] ; Guangdong Natural Science Foundation[2017A030312008]

Computer Science ; Education & Educational Research

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Education, Scientific Disciplines

WOS:000667722803064

20212210428389

Deep neural networks ; Electrocardiography

Medicine and Pharmacology:461.6 ; Artificial Intelligence:723.4

2-s2.0-85100347473

Scopus

1.The Hong Kong University of Science and Technology,
2.Southern University of Science and Technology,Peng Cheng Laboratory,
3.Huazhong University of Science and Technology,

Chen，Huangxun,Huang，Chenyu,Huang，Qianyi,et al. Ecgadv: Generating adversarial electrocardiogram to misguide arrhythmia classification system[C]. 2275 E BAYSHORE RD, STE 160, PALO ALTO, CA 94303 USA:ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE,2020:3446-3453.