Synthetic Data Generation¶
Definition¶
Synthetic data generation creates artificial training data using generative models or rendering engines — enabling training on scenarios that are rare, expensive, or impossible to collect in the real world.
Generation Methods¶
| Method | How It Works | Quality | eKYC Use |
|---|---|---|---|
| GAN-based | Train GAN to generate realistic images | High | Synthetic faces, synthetic attacks |
| Diffusion-based | Stable Diffusion/DALL-E generate from prompts | Very High | Document variations, scene generation |
| 3D rendering | Render faces/documents with controlled parameters | Controllable | Face liveness (3D face + spoof simulation) |
| Rule-based | Programmatic manipulation of real images | Variable | Synthetic tampered documents |
| Digital twin | Simulate complete capture environment | High | Camera + lighting + attack simulation |
Applications in eKYC¶
| Application | Synthetic Data Type |
|---|---|
| Liveness training | Synthetic spoof images (simulated print/screen/mask) |
| Face recognition | Synthetic faces for privacy-compliant training |
| Document forensics | Synthetically tampered documents (known ground truth) |
| Document OCR | Synthetic text on document backgrounds |
| Bias mitigation | Generate underrepresented demographic groups |
Key Takeaways¶
Summary
- Synthetic data solves data scarcity (rare attacks), privacy (no real PII), and bias (balanced demographics)
- Diffusion models produce the highest-quality synthetic images currently
- Known ground truth is the biggest advantage — every pixel of synthetic data has a label
- Risk: domain gap between synthetic and real data can limit model performance
- Best practice: mix synthetic + real data rather than training on synthetic alone