Application of Adversarial Examples to Physical ECG Signals

• URL: http://arxiv.org/abs/2108.08972v1
• Date: Fri, 20 Aug 2021 02:30:17 GMT
• Title: Application of Adversarial Examples to Physical ECG Signals
• Authors: Taiga Ono (1), Takeshi Sugawara (2), Jun Sakuma (3), Tatsuya Mori (1 and 4) ((1) Waseda University, (2) The University of Electro-Communications, (3) University of Tsukuba, (4) RIKEN AIP)
• Abstract summary: We introduce adversarial beats, which are perturbations tailored specifically against electrocardiograms (ECGs) beat-by-beat classification system. We first formulate an algorithm to generate adversarial examples for the ECG classification neural network model, and study its attack success rate. We then mount a hardware attack by designing a malicious signal generator which injects adversarial beats into ECG sensor readings.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work aims to assess the reality and feasibility of the adversarial attack against cardiac diagnosis system powered by machine learning algorithms. To this end, we introduce adversarial beats, which are adversarial perturbations tailored specifically against electrocardiograms (ECGs) beat-by-beat classification system. We first formulate an algorithm to generate adversarial examples for the ECG classification neural network model, and study its attack success rate. Next, to evaluate its feasibility in a physical environment, we mount a hardware attack by designing a malicious signal generator which injects adversarial beats into ECG sensor readings. To the best of our knowledge, our work is the first in evaluating the proficiency of adversarial examples for ECGs in a physical setup. Our real-world experiments demonstrate that adversarial beats successfully manipulated the diagnosis results 3-5 times out of 40 attempts throughout the course of 2 minutes. Finally, we discuss the overall feasibility and impact of the attack, by clearly defining motives and constraints of expected attackers along with our experimental results.

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