Input: figure image and optional caption.
Output:
{
"decision": "authentic | manipulated",
"confidence": 0.0
}Metrics:
- Accuracy
- Macro-F1
- False positive rate
- False negative rate
Input: figure image, caption, and manipulation taxonomy choices.
Output:
{
"decision": "manipulated",
"manipulation_type": "legend_color_mismatch",
"confidence": 0.0
}Metrics:
- Macro-F1 across manipulation types
- Per-type precision, recall, and F1
- Confusion matrix by manipulation type
Input: figure image and caption.
Output:
{
"decision": "manipulated",
"manipulation_type": "caption_figure_inconsistency",
"evidence": "The caption claims Method A is best, but the tallest bar is Method C."
}Metrics:
- Explanation accuracy: whether the explanation identifies the correct inconsistency.
- Evidence consistency: whether the explanation supports the predicted label.
- Human-checkable evidence score: 0 for wrong, 1 for partially correct, 2 for correct and specific.
| Baseline | Description | Expected value |
|---|---|---|
| VLM-only | Directly ask a VLM whether the figure is manipulated | Measures default visual reasoning |
| VLM + checklist prompt | Ask the model to inspect axes, labels, legends, captions, and visual values before deciding | Tests prompt-level assistance |
| OCR/parser + rules | Extract text and metadata-like structure, then check consistency rules | Provides interpretable non-training baseline |
| FigCheck | Figure-type-aware checklist plus VLM or rule verification | Main lightweight method |
Run the test set under simple perturbations:
- JPEG compression
- Downsampling
- Slight blur
- Screenshot-style resizing
Report whether each baseline is brittle under post-processing.
Each experiment should produce:
- Overall metrics table.
- Metrics by figure type.
- Metrics by manipulation type.
- Failure examples with image, prediction, ground truth, and explanation.
- Short interpretation of the dominant failure modes.