Card Anomaly Detection Eval

---
name: card-anomaly-detection-eval
description: Evaluates the robustness and energy efficiency of a neuromorphic spiking neural network for real-time anomaly detection on lunar rover sensor telemetry. It specifically probes the model's ability to maintain classification accuracy under gradient-based and temporal adversarial attacks while measuring hardware-level power consumption. Use when the user wants to benchmark on Cislunar Anomaly and Risk Dataset (CARD), or asks about evaluating this task. Reports Adversarial Success Rate (ASR).
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2603.13880
  bibtex_key: kaczmarek2026benchmarking
  confidence: high
---

# card-anomaly-detection-eval

> Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics — Kaczmarek (2026) (arXiv:2603.13880, 2026)

## What this evaluates

Evaluates the robustness and energy efficiency of a neuromorphic spiking neural network for real-time anomaly detection on lunar rover sensor telemetry. It specifically probes the model's ability to maintain classification accuracy under gradient-based and temporal adversarial attacks while measuring hardware-level power consumption.

## Datasets

- **Cislunar Anomaly and Risk Dataset (CARD)** — total ?; splits: test (-1)

## Metrics

- `Adversarial Success Rate (ASR)` **(primary)** — range: percent
  - Percentage of attack attempts that successfully cause the model to misclassify an anomaly as a nominal state.
- `Energy per Inference` — range: other
  - Integrated instantaneous power draw over the inference window, divided by the number of samples processed. Dynamic energy subtracts an idle baseline.
- `Latency` — range: other
  - Wall-clock time required for the hardware to process a single input window and output a classification event, excluding PCIe transfer overhead.

## Input / output format

**Input**: Streaming multi-modal sensor telemetry (IMU data and visual feeds) processed as input windows for real-time classification.

**Output**: Binary classification event indicating system health (nominal vs. anomaly).

## Scoring recipe

```python
def compute_asr(predictions, gold_labels, attack_mask):
    successful = sum(1 for p, g, a in zip(predictions, gold_labels, attack_mask) if a and p != g and g == 'anomaly')
    return (successful / sum(attack_mask)) * 100

def compute_energy(power_samples, sample_rate, num_samples, idle_baseline):
    total_energy = sum(power_samples) / sample_rate
    return (total_energy / num_samples) - idle_baseline
```

## Common pitfalls

- Energy and latency measurements must exclude PCIe transfer overhead to isolate the neuromorphic processor's performance.
- Adversarial Success Rate (ASR) only counts misclassifications where an anomaly is incorrectly labeled as nominal, not all errors.
- All metrics are averaged over 10 independent runs and measured with batch size 1 to simulate real-time edge processing.

## Evidence (verbatim from paper)

> We define the Adversarial Success Rate (ASR) as the percentage of attack attempts that successfully cause the model to misclassify an anomaly as a nominal state. The total energy per inference is calculated by integrating the instantaneous power draw over the duration of the inference window and dividing by the number of samples processed.

## Citation

```bibtex
@misc{kaczmarek2026benchmarking,
  title={Benchmarking the Energy Cost of Assurance in Neuromorphic Edge Robotics},
  author={Kaczmarek (2026)},
  year={2026},
  note={arXiv:2603.13880}
}
```

- arXiv: 2603.13880