SCAR: Semantic Cardiac Adversarial Representation via Spatiotemporal Manifold Optimization in ECG
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| Format: | Preprint |
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2025
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| _version_ | 1866911327965937664 |
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| author | Jia, Shunbo Liao, Caizhi |
| author_facet | Jia, Shunbo Liao, Caizhi |
| contents | Deep learning models for Electrocardiogram (ECG) analysis have achieved expert-level performance but remain vulnerable to adversarial attacks. However, applying Universal Adversarial Perturbations (UAP) to ECG signals presents a unique challenge: standard imperceptible noise constraints (e.g., 10 uV) fail to generate effective universal attacks due to the high inter-subject variability of cardiac waveforms. Furthermore, traditional "invisible" attacks are easily dismissed by clinicians as technical artifacts, failing to compromise the human-in-the-loop diagnostic pipeline. In this study, we propose SCAR (Semantic Cardiac Adversarial Representation), a novel UAP framework tailored to bypass the clinical "Human Firewall." Unlike traditional approaches, SCAR integrates spatiotemporal smoothing (W=25, approx. 50ms), spectral consistency (<15 Hz), and anatomical amplitude constraints (<0.2 mV) directly into the gradient optimization manifold.
Results: We benchmarked SCAR against a rigorous baseline (Standard Universal DeepFool with post-hoc physiological filtering). While the baseline suffers a performance collapse (~16% success rate on transfer tasks), SCAR maintains robust transferability (58.09% on ResNet) and achieves 82.46% success on the source model. Crucially, clinical analysis reveals an emergent targeted behavior: SCAR specifically converges to forging Myocardial Infarction features (90.2% misdiagnosis) by mathematically reconstructing pathological ST-segment elevations. Finally, we demonstrate that SCAR serves a dual purpose: it not only functions as a robust data augmentation strategy for Hybrid Adversarial Training, offering optimal clinical defense, but also provides effective educational samples for training clinicians to recognize low-cost, AI-targeted semantic forgeries. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_17423 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SCAR: Semantic Cardiac Adversarial Representation via Spatiotemporal Manifold Optimization in ECG Jia, Shunbo Liao, Caizhi Signal Processing Deep learning models for Electrocardiogram (ECG) analysis have achieved expert-level performance but remain vulnerable to adversarial attacks. However, applying Universal Adversarial Perturbations (UAP) to ECG signals presents a unique challenge: standard imperceptible noise constraints (e.g., 10 uV) fail to generate effective universal attacks due to the high inter-subject variability of cardiac waveforms. Furthermore, traditional "invisible" attacks are easily dismissed by clinicians as technical artifacts, failing to compromise the human-in-the-loop diagnostic pipeline. In this study, we propose SCAR (Semantic Cardiac Adversarial Representation), a novel UAP framework tailored to bypass the clinical "Human Firewall." Unlike traditional approaches, SCAR integrates spatiotemporal smoothing (W=25, approx. 50ms), spectral consistency (<15 Hz), and anatomical amplitude constraints (<0.2 mV) directly into the gradient optimization manifold. Results: We benchmarked SCAR against a rigorous baseline (Standard Universal DeepFool with post-hoc physiological filtering). While the baseline suffers a performance collapse (~16% success rate on transfer tasks), SCAR maintains robust transferability (58.09% on ResNet) and achieves 82.46% success on the source model. Crucially, clinical analysis reveals an emergent targeted behavior: SCAR specifically converges to forging Myocardial Infarction features (90.2% misdiagnosis) by mathematically reconstructing pathological ST-segment elevations. Finally, we demonstrate that SCAR serves a dual purpose: it not only functions as a robust data augmentation strategy for Hybrid Adversarial Training, offering optimal clinical defense, but also provides effective educational samples for training clinicians to recognize low-cost, AI-targeted semantic forgeries. |
| title | SCAR: Semantic Cardiac Adversarial Representation via Spatiotemporal Manifold Optimization in ECG |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2512.17423 |