aiqbench analysis
This research exposes a critical vulnerability in adaptive conformal prediction systems used for ECG monitoring, where manipulating the temporal order of authentic events can suppress life-saving ventricular class detections without altering any underlying data or model weights. The study demonstrates that even standard machine learning classifiers like Extra Trees and HistGradientBoosting are susceptible to such timing-based attacks, significantly lowering safety thresholds before a target event is evaluated. For AI in healthcare readers, this highlights the necessity of robust defenses against adversarial perturbations in input sequencing for real-time medical monitoring systems.
ConformalShift: Targeted Event Reordering Against Adaptive ECG Monitoring
arXiv:2608.03628v1 Announce Type: new Abstract: Adaptive conformal prediction can recover clinically important heartbeat classes missed by a point classifier, but delayed feedback makes its decisions sensitive to event order. We introduce ConformalShift, a bounded event-reordering attack that suppresses the ventricular class for rescued events without modifying ECG waveforms, labels, classifier scores, or the event multiset. ConformalShift searches for feasible permutations of authentic preceding events that lower the ventricular threshold before a selected target is evaluated. On disjoint MIT--BIH confirmation records, the attack suppressed 66.7% of eligible targets for Extra Trees and 60.0% for HistGradientBoosting, compared with random-schedule rates of 4.4% and 12.0%, respectively. Transferred configurations also outperformed random scheduling on INCART, while reducing the displacement budget weakened the attack on both datasets. These results show that adaptive monitors in healthcare can be compromised through the timing of authentic information, even when waveforms, labels, classifier outputs, and event contents remain unchanged.
Abstract:Adaptive conformal prediction can recover clinically important heartbeat classes missed by a point classifier, but delayed feedback makes its decisions sensitive to event order. We introduce ConformalShift, a bounded event-reordering attack that suppresses the ventricular class for rescued events without modifying ECG waveforms, labels, classifier scores, or the event multiset. ConformalShift searches for feasible permutations of authentic preceding events that lower the ventricular threshold before a selected target is evaluated. On disjoint MIT--BIH confirmation records, the attack suppressed 66.7% of eligible targets for Extra Trees and 60.0% for HistGradientBoosting, compared with random-schedule rates of 4.4% and 12.0%, respectively. Transferred configurations also outperformed random scheduling on INCART, while reducing the displacement budget weakened the attack on both datasets. These results show that adaptive monitors in healthcare can be compromised through the timing of authentic information, even when waveforms, labels, classifier outputs, and event contents remain unchanged.
Machine Learning (cs.LG)
arXiv:2608.03628 [cs.LG]
(or arXiv:2608.03628v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.03628 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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