Definition
False positives are cases where an image or video analysis system incorrectly identifies a region as containing sensitive data (e.g. a face, license plate), although no such object is present. In anonymization workflows, this results in unnecessary masking or redaction of non-sensitive visual elements.
These misclassifications reduce content quality and may interfere with the usability of processed materials.
Causes of false positives
Cause | Description |
Visual noise and artefacts | Compression noise, glare or distortions falsely triggering detectors |
Complex backgrounds | Patterns or objects mimicking sensitive shapes |
Unusual non-sensitive objects | Textures or graphics resembling faces or text |
Low detection threshold | Over-sensitive models with low confidence cutoff |
Model bias or overfitting | Insufficiently generalized models due to limited training scope |
Impact on anonymization
- Over-anonymization - irrelevant areas are blurred or redacted
- Loss of visual clarity - non-sensitive content is obscured
- Distortion of analytical outcomes - affects downstream visual analytics workflows
- Decreased system trust - perceived as overly aggressive or inaccurate
- Increased processing load - higher computational cost without value gain
Minimizing false positives
Method | Description |
Improved training data | Diverse and realistic examples reduce misclassifications |
Threshold tuning | Optimizing the trade-off between sensitivity and precision |
Ensemble validation | Cross-verification by multiple models to confirm detection |
Post-processing filters | Heuristic checks for size, shape or context of detections |
Human QA review | Periodic manual inspection of anonymization outputs |
Examples
- Blurring a cartoon face on a poster misclassified as real
- Masking decorative items resembling human forms
- Obscuring corporate logos with stylized human figures