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Confidence engine — decomposes every score into weighted sub-scores
with a human-readable explanation.
Sub-scores:
- source_reliability (provider's track record)
- cross_provider_consensus (how many providers agree)
- detection_confidence (raw provider confidence)
- latency_penalty (slower = lower confidence)
"""
from __future__ import annotations
from typing import Any, List, Protocol
from models.reports import (
ConfidenceScore,
ConflictReport,
FaceDetection,
FaceMatch,
ForensicsResult,
ImageAnalysisResult,
)
WEIGHTS = {
"source_reliability": 0.25,
"cross_provider_consensus": 0.30,
"detection_confidence": 0.35,
"latency_penalty": 0.10,
}
# Default reliability per provider (can be overridden by metrics history)
DEFAULT_RELIABILITY = {
"haar": 0.65,
"dnn": 0.85,
"mtcnn": 0.92,
"retinaface": 0.95,
"face_recognition": 0.90,
"deepface": 0.88,
"insightface": 0.93,
"beautifulsoup": 0.75,
"selenium": 0.70,
"bing": 0.85,
"duckduckgo": 0.65,
"google_lens": 0.55,
"serpapi": 0.90,
"yandex": 0.55,
"tineye": 0.88,
# analysis / metadata / forensics
"image_quality": 0.85,
"image_properties": 0.85,
"visual_features": 0.80,
"exif": 0.95,
"xmp": 0.90,
"image_integrity": 0.90,
"duplicate_detector": 0.85,
"manipulation_analyzer": 0.75,
}
# Duck-typed protocols (confidence must not import from normalization)
class _BoxLike(Protocol):
detector: str
confidence: float
class _MatchLike(Protocol):
query_face_index: int
best_match: str | None
distance: float
recognizer: str
class _AnalysisLike(Protocol):
provider: str
quality_score: float | None
class _MetadataLike(Protocol):
provider: str
format: str | None
class _ForensicsLike(Protocol):
provider: str
integrity_score: float | None
class ConfidenceEngine:
"""Computes weighted confidence scores."""
def __init__(self, reliability_overrides: dict | None = None) -> None:
self._reliability = {**DEFAULT_RELIABILITY, **(reliability_overrides or {})}
# ------------------------------------------------------------------ #
# Detection confidence
# ------------------------------------------------------------------ #
def score_detection(
self,
nbox: _BoxLike,
all_results: dict[str, Any],
) -> ConfidenceScore:
provider = nbox.detector
source_rel = self._reliability.get(provider, 0.7)
num_detectors = sum(
1 for r in all_results.values()
if r.success and r.capability.value == "detection"
)
consensus = min(1.0, num_detectors / 3.0)
det_conf = nbox.confidence
provider_result = all_results.get(provider)
latency = provider_result.elapsed_ms if provider_result else 500.0
latency_score = max(0.5, 1.0 - (latency / 4000.0))
components = {
"source_reliability": round(source_rel, 4),
"cross_provider_consensus": round(consensus, 4),
"detection_confidence": round(det_conf, 4),
"latency_penalty": round(latency_score, 4),
}
overall = sum(components[k] * WEIGHTS[k] for k in WEIGHTS)
explanation = (
f"{provider} detected a face with raw confidence {det_conf:.2f}. "
f"{num_detectors} detector(s) ran in this job (consensus={consensus:.2f}). "
f"Provider reliability={source_rel:.2f}, latency={latency:.0f}ms (score={latency_score:.2f})."
)
return ConfidenceScore(
overall=round(overall, 4),
components=components,
explanation=explanation,
method="weighted_average",
)
# ------------------------------------------------------------------ #
# Recognition match confidence
# ------------------------------------------------------------------ #
def score_match(self, nm: _MatchLike) -> ConfidenceScore:
provider = nm.recognizer
source_rel = self._reliability.get(provider, 0.7)
match_conf = max(0.0, 1.0 - nm.distance)
components = {
"source_reliability": round(source_rel, 4),
"cross_provider_consensus": 0.5,
"detection_confidence": round(match_conf, 4),
"latency_penalty": 0.9,
}
overall = sum(components[k] * WEIGHTS[k] for k in WEIGHTS)
explanation = (
f"{provider} matched face #{nm.query_face_index} "
f"to '{nm.best_match}' with distance {nm.distance:.3f} "
f"(match confidence={match_conf:.2f})."
)
return ConfidenceScore(
overall=round(overall, 4),
components=components,
explanation=explanation,
method="weighted_average",
)
# ------------------------------------------------------------------ #
# Image analysis confidence
# ------------------------------------------------------------------ #
def score_image_analysis(self, nia: _AnalysisLike) -> ConfidenceScore:
provider = nia.provider
source_rel = self._reliability.get(provider, 0.8)
# If the provider returned a quality_score use it, else neutral 0.7
quality = nia.quality_score if nia.quality_score is not None else 0.7
components = {
"source_reliability": round(source_rel, 4),
"cross_provider_consensus": 0.7,
"detection_confidence": round(quality, 4),
"latency_penalty": 0.9,
}
overall = sum(components[k] * WEIGHTS[k] for k in WEIGHTS)
explanation = (
f"{provider} analyzed image with quality score {quality:.2f}. "
f"Provider reliability={source_rel:.2f}."
)
return ConfidenceScore(
overall=round(overall, 4),
components=components,
explanation=explanation,
method="weighted_average",
)
# ------------------------------------------------------------------ #
# Metadata confidence
# ------------------------------------------------------------------ #
def score_metadata(self, nm: _MetadataLike) -> ConfidenceScore:
provider = nm.provider
source_rel = self._reliability.get(provider, 0.9)
# Higher confidence if we actually extracted a format
extraction_conf = 0.95 if nm.format else 0.5
components = {
"source_reliability": round(source_rel, 4),
"cross_provider_consensus": 0.8,
"detection_confidence": round(extraction_conf, 4),
"latency_penalty": 0.95,
}
overall = sum(components[k] * WEIGHTS[k] for k in WEIGHTS)
explanation = (
f"{provider} extracted metadata (format={nm.format or 'unknown'}). "
f"Provider reliability={source_rel:.2f}."
)
return ConfidenceScore(
overall=round(overall, 4),
components=components,
explanation=explanation,
method="weighted_average",
)
# ------------------------------------------------------------------ #
# Forensics confidence
# ------------------------------------------------------------------ #
def score_forensics(self, nf: _ForensicsLike) -> ConfidenceScore:
provider = nf.provider
source_rel = self._reliability.get(provider, 0.8)
# Use integrity_score if present, else fall back to neutral
integrity = nf.integrity_score if nf.integrity_score is not None else 0.7
components = {
"source_reliability": round(source_rel, 4),
"cross_provider_consensus": 0.6,
"detection_confidence": round(integrity, 4),
"latency_penalty": 0.85,
}
overall = sum(components[k] * WEIGHTS[k] for k in WEIGHTS)
explanation = (
f"{provider} forensics analysis integrity_score={integrity:.2f}. "
f"Provider reliability={source_rel:.2f}."
)
return ConfidenceScore(
overall=round(overall, 4),
components=components,
explanation=explanation,
method="weighted_average",
)
# ------------------------------------------------------------------ #
# Overall report confidence
# ------------------------------------------------------------------ #
def score_overall(
self,
report_detections: List[FaceDetection] = None,
matches: List[FaceMatch] = None,
conflicts: List[ConflictReport] = None,
image_analyses: List[ImageAnalysisResult] = None,
forensics: List[ForensicsResult] = None,
) -> ConfidenceScore:
report_detections = report_detections or []
matches = matches or []
conflicts = conflicts or []
image_analyses = image_analyses or []
forensics = forensics or []
# Collect all sub-scores available
subscores: list[float] = []
if report_detections:
subscores.append(sum(d.confidence.overall for d in report_detections) / len(report_detections))
if matches:
subscores.append(sum(m.confidence.overall for m in matches) / len(matches))
if image_analyses:
subscores.append(sum(a.confidence.overall for a in image_analyses if a.confidence) / len(image_analyses))
if forensics:
subscores.append(sum(f.confidence.overall for f in forensics if f.confidence) / len(forensics))
if not subscores:
return ConfidenceScore(
overall=0.0,
components={},
explanation="No results produced.",
)
base_avg = sum(subscores) / len(subscores)
conflict_penalty = max(0.0, 1.0 - (0.15 * len(conflicts)))
overall = base_avg * 0.8 + conflict_penalty * 0.2
components = {
"result_avg": round(base_avg, 4),
"conflict_penalty": round(conflict_penalty, 4),
"num_detections": float(len(report_detections)),
"num_matches": float(len(matches)),
"num_image_analyses": float(len(image_analyses)),
"num_forensics": float(len(forensics)),
}
explanation = (
f"Report confidence from {len(report_detections)} detection(s), "
f"{len(matches)} match(es), {len(image_analyses)} image analyses, "
f"{len(forensics)} forensics results, {len(conflicts)} conflict(s)."
)
return ConfidenceScore(
overall=round(overall, 4),
components=components,
explanation=explanation,
method="weighted_average",
)
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