8f6506b70a
Major additions: - Staged scanning modes (working tree, staged, committed) with git integration - Drift detection for baseline vs current state comparisons - Hosted API handlers for policy CRUD operations via StemeDB API - stemedb-ontology crate with domain definitions and medical extractors - Consumer health vertical UAT scenarios (GLP-1, gastroparesis, etc.) - Aphoria development skill documentation Code organization: - Split large files into focused modules to stay under 500-line limit - Extracted config tests, episteme helpers/drift/aliases, API helpers Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
127 lines
3.3 KiB
Markdown
127 lines
3.3 KiB
Markdown
# UAT: Visual Anchoring (pHash Validation)
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**Date:** YYYY-MM-DD
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**Feature:** Perceptual Hash Provenance
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**Status:** [ ] PASS / [ ] FAIL / [ ] BLOCKED
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## Scenario
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An OCR-extracted claim from a PDF table needs validation against the original visual. The perceptual hash (pHash) of the source image allows:
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1. Detecting if the source has been tampered with
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2. Fuzzy-matching similar screenshots
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3. Provenance tracking to original visual evidence
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## Acceptance Criteria
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| Criterion | Expected | Met? |
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|-----------|----------|------|
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| Assertion stored with pHash | visual_hash populated | [ ] |
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| Same image = same pHash | Hamming distance = 0 | [ ] |
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| Similar image = close pHash | Hamming distance < 10 | [ ] |
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| Different image = far pHash | Hamming distance > 20 | [ ] |
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| Query by pHash similarity | Returns matching assertions | [ ] |
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## Test Matrix
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| Step | Action | Expected | Actual | Status |
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|------|--------|----------|--------|--------|
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| 1 | Ingest assertion with pHash | Hash returned | | [ ] |
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| 2 | Query by exact pHash | Assertion returned | | [ ] |
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| 3 | Query by similar pHash | Assertion returned (fuzzy) | | [ ] |
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| 4 | Query by different pHash | No match | | [ ] |
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## pHash Background
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Perceptual hashing creates a fingerprint of visual content that:
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- Survives JPEG compression
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- Survives minor cropping/resizing
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- Distinguishes semantically different images
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We use an 8-byte (64-bit) pHash. Hamming distance measures similarity:
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- 0 = identical
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- < 10 = visually similar
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- > 20 = different images
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## Setup Commands
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```bash
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# Start StemeDB
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cargo run --bin stemedb-api &
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sleep 2
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```
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## Test Commands
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### Step 1: Ingest Assertion with Visual Hash
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```bash
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# pHash of a hypothetical FDA label table screenshot
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# In real usage, this would be computed from the actual image
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PHASH_HEX="a1b2c3d4e5f60718"
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curl -X POST http://localhost:18180/v1/assertions \
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-H "Content-Type: application/json" \
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-d "{
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\"subject\": \"Semaglutide\",
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\"predicate\": \"adverse_event_rate\",
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\"object\": {\"Number\": 0.043},
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\"confidence\": 0.98,
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\"source_class\": \"Regulatory\",
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\"visual_hash\": \"$PHASH_HEX\"
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}"
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```
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**Expected:** Hash returned
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**Actual:**
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**Status:** [ ]
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### Step 2: Query by Exact pHash
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```bash
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curl "http://localhost:18180/v1/query?visual_hash=a1b2c3d4e5f60718"
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```
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**Expected:** Returns the assertion from Step 1
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**Actual:**
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**Status:** [ ]
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### Step 3: Query by Similar pHash (Hamming distance < 10)
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```bash
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# Slightly different pHash (2 bits flipped)
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curl "http://localhost:18180/v1/query?visual_hash=a1b2c3d4e5f60719&phash_threshold=10"
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```
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**Expected:** Returns the assertion (fuzzy match)
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**Actual:**
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**Status:** [ ]
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### Step 4: Query by Different pHash (Hamming distance > 20)
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```bash
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# Completely different pHash
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curl "http://localhost:18180/v1/query?visual_hash=1234567890abcdef&phash_threshold=10"
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```
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**Expected:** No results (too different)
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**Actual:**
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**Status:** [ ]
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## Sign-Off Checklist
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- [ ] visual_hash field stored in assertion
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- [ ] Exact pHash match works
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- [ ] Fuzzy pHash match within threshold works
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- [ ] Different pHash correctly excluded
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- [ ] pHash indexed for efficient lookup
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## Notes
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*pHash computation happens client-side (during extraction). StemeDB stores and indexes the hash but doesn't compute it.*
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---
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**Tester:**
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**Date:**
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**Result:**
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