SDB-26 Standard Page
A benchmark for document authenticity, not marketing accuracy.
SDB-26 defines reproducible evaluation for synthetic, edited, and screen-recaptured artifacts in operational conditions, with transparent metrics and schema-valid outputs.
Measurement Grid
SDB-26 is built around measurable, comparable outcomes:
| Metric | Meaning | Why it matters |
|---|---|---|
| BR (Bypass Rate) | Share of fraudulent/synthetic documents incorrectly approved | Core indicator of control failure |
| CG (Confidence Gap) | Mean confidence on wrongly approved cases | Detects overconfident error patterns |
| GS (Generator Sensitivity) | BR segmented by generator/model family | Shows where systems break first |
| FPR (False Positive Rate) | Share of genuine cases flagged as suspicious/fraud | Tracks customer/business impact |
Reference: STANDARD.md, METHODOLOGY.md, results_schema.json.
Attack Levels
SDB-26 evaluates three escalating attack classes:
- L1 — Standard Generation: direct AI-generated documents, no post-processing.
- L2 — Advanced Diffusion: fine-tuning/editing/metadata manipulation scenarios.
- L3 — Screen Recapture: synthetic/edited files recaptured through display pipelines.
L3 is a foundation layer in the methodology because recapture can remove or distort provenance cues while preserving plausible visual content.
Audit Trails
SDB-26 includes FRC and A2A artifacts for auditable decisions in agent-mediated workflows:
docs/FRC_OVERVIEW.mddocs/FRC_A2A_EXTENSION.mddocs/FRC_A2A_DEPLOYMENT_MAPPING.md
This bridges document-level authenticity with agent-era traceability
(instrumentation_trace, L0/L0-D signals, ABR/TCR/HAR-style controls).
Reference Implementation
Practical implementation path:
-
Forensic packet collection workflow (
collect_forensic_packet.py) for repeatable corpus acquisition pipelines. - Schema-valid decision artifacts using FRC/FRC A2A outputs and fixtures in this repository.
Related repo artifacts:
examples/frc/tests/frc/schemas/frc_schema_v1_0_0.jsonschemas/frc_a2a_envelope_v0_2_0.json
Why Now
As AI generation quality and agent-mediated onboarding velocity rise, trust controls must move from static checks to measurable, reproducible evidence chains.
SDB-26 provides that measurement contract.