Synthetic Document Detection¶
Definition¶
Synthetic document detection identifies identity documents that are entirely AI-generated — created from scratch using generative AI rather than altered from genuine documents. As AI image generation improves, fully synthetic fake IDs are becoming a growing threat.
Generation Methods¶
| Method | Quality | Cost |
|---|---|---|
| Photoshop templates | Medium | Low — templates available on dark web |
| GAN-generated | High | Medium — requires training data |
| Stable Diffusion / DALL-E | Variable-High | Low — text-to-image prompts |
| Specialized forgery tools | Very High | Medium — purpose-built for document fraud |
Detection Approaches¶
| Approach | What It Detects |
|---|---|
| GAN artifact analysis | Frequency-domain artifacts from GAN generation |
| Consistency checks | Font consistency, alignment precision, security feature presence |
| Template matching | Compare layout/design against known genuine templates |
| Physical cue absence | Real documents have micro-variations from printing — synthetics are too perfect |
| Cross-database verification | Verify document number exists in government database |
Key Takeaways¶
Summary
- AI-generated fake IDs are an emerging threat — generation quality is improving rapidly
- Detection combines GAN artifact analysis, template consistency, and database verification
- Database verification is the strongest defense — a fake document number won't exist in government records
- This is a rapidly evolving threat requiring continuous model updates