# Joint Face Spoofing Forgery Eval

> Evaluates models' ability to detect physical face spoofing attacks and digital face forgeries using visual appearance and physiological rPPG cues. It measures cross-domain generalization and compares separate versus joint multi-task learning protocols. Use when the user wants to benchmark on SiW, 3DMAD, HKBU-MarsV2, MSU-MFSD, 3DMask, ROSE-Youtu, FaceForensics++, DFDC, CelebDFv2, or asks about evaluating this task. Reports AUC, EER.

- Skill: `qhjqhj00/joint-face-spoofing-forgery-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/joint-face-spoofing-forgery-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/joint-face-spoofing-forgery-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/joint-face-spoofing-forgery-eval

---


# joint-face-spoofing-forgery-eval

> Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues — Yu et al. (2022) (arXiv:2208.05401, 2022)

## What this evaluates

Evaluates models' ability to detect physical face spoofing attacks and digital face forgeries using visual appearance and physiological rPPG cues. It measures cross-domain generalization and compares separate versus joint multi-task learning protocols.

## Datasets

- **SiW** — total ?; splits: train (-1), test (-1)
- **3DMAD** — total ?; splits: train (-1), test (-1)
- **HKBU-MarsV2** — total ?; splits: train (-1), test (-1)
- **MSU-MFSD** — total ?; splits: test (-1)
- **3DMask** — total ?; splits: test (-1)
- **ROSE-Youtu** — total ?; splits: test (-1)
- **FaceForensics++** — total ?; splits: train (-1), test (-1)
- **DFDC** — total ?; splits: test (-1)
- **CelebDFv2** — total ?; splits: test (-1)

## Metrics

- `AUC` **(primary)** — range: percent
  - Area Under the Receiver Operating Characteristic Curve. Measures the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.
- `EER` **(primary)** — range: percent
  - Equal Error Rate, the operating point where False Acceptance Rate equals False Rejection Rate.
- `TPR@FPR=10%` — range: percent
  - True Positive Rate calculated at a fixed False Positive Rate of 10%.
- `TPR@FPR=1%` — range: percent
  - True Positive Rate calculated at a fixed False Positive Rate of 1%.

## Input / output format

**Input**: Video sequences of faces (typically >5 seconds) containing either bonafide subjects or attack/forgery samples. Inputs may include raw RGB frames, extracted rPPG signals, MSTmaps, or WaveletMaps.

**Output**: Binary classification score/probability for bonafide vs. spoof/forgery, optionally with a task identifier (spoofing or forgery) for joint training.

## Scoring recipe

```python
def compute_metrics(predictions, labels):
    fpr, tpr, _ = roc_curve(labels, predictions)
    auc = auc(fpr, tpr)
    eer = tpr[np.argmin(np.abs(fpr - tpr))]
    tpr_10 = interp(0.10, fpr, tpr)
    tpr_1 = interp(0.01, fpr, tpr)
    return {'AUC': auc, 'EER': eer, 'TPR@FPR=10%': tpr_10, 'TPR@FPR=1%': tpr_1}
```

## Common pitfalls

- Cross-domain evaluation strictly uses entirely unseen datasets for testing without any training data from those domains, making it a zero-shot domain-shift test rather than standard cross-validation.
- Joint training shares parameters across two distinct tasks (physical spoofing vs. digital forgery), which can lead to task interference if modality biases are not explicitly mitigated.
- rPPG extraction is highly sensitive to lighting conditions and head motion, often requiring specific preprocessing that varies across datasets and can skew physiological cue performance.

## Evidence (verbatim from paper)

> For all experiments, Area Under Curve (AUC) and Equal Error Rate (EER) are utilized for performance evaluation. Besides, the evaluations under intra- and cross-domain scenarios among multiple (merged) datasets are investigated by using the metric of single-side True Positive Rate (TPR)@False Positive Rate (FPR), which is more suitable for realistic spectacles.

## Citation

```bibtex
@misc{yu2022benchmarking,
  title={Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues},
  author={Yu et al. (2022)},
  year={2022},
  note={arXiv:2208.05401}
}
```

- arXiv: 2208.05401