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from typing import Dict
from typing import Tuple
from config.enums import Domain
from config.enums import ConfidenceLevel
from config.schemas import MetricThresholds
from config.schemas import DomainThresholds
# ================================ OPTIMIZED THRESHOLDS ===============================
# Philosophy: Multi-perturbation stability and perplexity are the most robust signals
# =====================================================================================
# GENERAL (Default fallback) - Balanced
DEFAULT_THRESHOLDS = DomainThresholds(domain = Domain.GENERAL,
structural = MetricThresholds(synthetic_threshold = 0.53, authentic_threshold = 0.38, weight = 0.15, confidence_multiplier = 1.0),
perplexity = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.40, weight = 0.23, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.46, authentic_threshold = 0.44, weight = 0.16, confidence_multiplier = 1.0),
semantic = MetricThresholds(synthetic_threshold = 0.53, authentic_threshold = 0.38, weight = 0.13, confidence_multiplier = 1.0),
linguistic = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.35, weight = 0.10, confidence_multiplier = 1.0),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.58, authentic_threshold = 0.33, weight = 0.23, confidence_multiplier = 1.0),
ensemble_threshold = 0.30,
)
# ACADEMIC
ACADEMIC_THRESHOLDS = DomainThresholds(domain = Domain.ACADEMIC,
structural = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.35, weight = 0.13, confidence_multiplier = 1.0),
perplexity = MetricThresholds(synthetic_threshold = 0.48, authentic_threshold = 0.38, weight = 0.22, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.44, authentic_threshold = 0.43, weight = 0.14, confidence_multiplier = 1.0),
semantic = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.35, weight = 0.14, confidence_multiplier = 1.0),
linguistic = MetricThresholds(synthetic_threshold = 0.60, authentic_threshold = 0.31, weight = 0.12, confidence_multiplier = 1.0),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.63, authentic_threshold = 0.28, weight = 0.25, confidence_multiplier = 1.0),
ensemble_threshold = 0.32,
)
# CREATIVE WRITING (creative writing is naturally unstable)
CREATIVE_THRESHOLDS = DomainThresholds(domain = Domain.CREATIVE,
structural = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.41, weight = 0.18, confidence_multiplier = 1.0),
perplexity = MetricThresholds(synthetic_threshold = 0.54, authentic_threshold = 0.43, weight = 0.20, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.48, weight = 0.20, confidence_multiplier = 1.0),
semantic = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.41, weight = 0.15, confidence_multiplier = 1.0),
linguistic = MetricThresholds(synthetic_threshold = 0.54, authentic_threshold = 0.38, weight = 0.09, confidence_multiplier = 1.0),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.57, authentic_threshold = 0.35, weight = 0.18, confidence_multiplier = 0.95),
ensemble_threshold = 0.33,
)
# AI/ML/DATA SCIENCE
AI_ML_THRESHOLDS = DomainThresholds(domain = Domain.AI_ML,
structural = MetricThresholds(synthetic_threshold = 0.46, authentic_threshold = 0.43, weight = 0.13, confidence_multiplier = 0.95),
perplexity = MetricThresholds(synthetic_threshold = 0.40, authentic_threshold = 0.46, weight = 0.24, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.36, authentic_threshold = 0.50, weight = 0.14, confidence_multiplier = 0.95),
semantic = MetricThresholds(synthetic_threshold = 0.46, authentic_threshold = 0.43, weight = 0.14, confidence_multiplier = 1.0),
linguistic = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.39, weight = 0.10, confidence_multiplier = 0.95),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.53, authentic_threshold = 0.36, weight = 0.25, confidence_multiplier = 1.0),
ensemble_threshold = 0.28,
)
# SOFTWARE DEVELOPMENT
SOFTWARE_DEV_THRESHOLDS = DomainThresholds(domain = Domain.SOFTWARE_DEV,
structural = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.35, weight = 0.14, confidence_multiplier = 0.90),
perplexity = MetricThresholds(synthetic_threshold = 0.48, authentic_threshold = 0.38, weight = 0.22, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.44, authentic_threshold = 0.43, weight = 0.15, confidence_multiplier = 0.90),
semantic = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.35, weight = 0.13, confidence_multiplier = 0.90),
linguistic = MetricThresholds(synthetic_threshold = 0.58, authentic_threshold = 0.33, weight = 0.11, confidence_multiplier = 0.85),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.61, authentic_threshold = 0.30, weight = 0.25, confidence_multiplier = 0.95),
ensemble_threshold = 0.31,
)
# TECHNICAL DOCUMENTATION (docs can be template-based)
TECHNICAL_DOC_THRESHOLDS = DomainThresholds(domain = Domain.TECHNICAL_DOC,
structural = MetricThresholds(synthetic_threshold = 0.52, authentic_threshold = 0.38, weight = 0.15, confidence_multiplier = 0.85),
perplexity = MetricThresholds(synthetic_threshold = 0.46, authentic_threshold = 0.40, weight = 0.24, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.42, authentic_threshold = 0.45, weight = 0.16, confidence_multiplier = 0.90),
semantic = MetricThresholds(synthetic_threshold = 0.52, authentic_threshold = 0.38, weight = 0.14, confidence_multiplier = 0.90),
linguistic = MetricThresholds(synthetic_threshold = 0.55, authentic_threshold = 0.35, weight = 0.11, confidence_multiplier = 0.85),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.58, authentic_threshold = 0.32, weight = 0.20, confidence_multiplier = 0.90),
ensemble_threshold = 0.30,
)
# ENGINEERING
ENGINEERING_THRESHOLDS = DomainThresholds(domain = Domain.ENGINEERING,
structural = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.35, weight = 0.14, confidence_multiplier = 0.85),
perplexity = MetricThresholds(synthetic_threshold = 0.48, authentic_threshold = 0.38, weight = 0.22, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.44, authentic_threshold = 0.43, weight = 0.15, confidence_multiplier = 0.90),
semantic = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.35, weight = 0.13, confidence_multiplier = 0.90),
linguistic = MetricThresholds(synthetic_threshold = 0.59, authentic_threshold = 0.32, weight = 0.11, confidence_multiplier = 0.85),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.62, authentic_threshold = 0.29, weight = 0.25, confidence_multiplier = 0.95),
ensemble_threshold = 0.30,
)
# SCIENCE (Physics, Chemistry, Biology)
SCIENCE_THRESHOLDS = DomainThresholds(domain = Domain.SCIENCE,
structural = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.35, weight = 0.13, confidence_multiplier = 0.90),
perplexity = MetricThresholds(synthetic_threshold = 0.49, authentic_threshold = 0.39, weight = 0.23, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.44, authentic_threshold = 0.43, weight = 0.15, confidence_multiplier = 0.90),
semantic = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.35, weight = 0.14, confidence_multiplier = 0.95),
linguistic = MetricThresholds(synthetic_threshold = 0.60, authentic_threshold = 0.31, weight = 0.11, confidence_multiplier = 0.90),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.62, authentic_threshold = 0.29, weight = 0.24, confidence_multiplier = 0.95),
ensemble_threshold = 0.32,
)
# BUSINESS
BUSINESS_THRESHOLDS = DomainThresholds(domain = Domain.BUSINESS,
structural = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.37, weight = 0.15, confidence_multiplier = 1.0),
perplexity = MetricThresholds(synthetic_threshold = 0.46, authentic_threshold = 0.41, weight = 0.22, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.42, authentic_threshold = 0.45, weight = 0.16, confidence_multiplier = 1.0),
semantic = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.37, weight = 0.14, confidence_multiplier = 1.0),
linguistic = MetricThresholds(synthetic_threshold = 0.54, authentic_threshold = 0.33, weight = 0.10, confidence_multiplier = 0.95),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.31, weight = 0.23, confidence_multiplier = 0.95),
ensemble_threshold = 0.28,
)
# LEGAL (NO CHANGES - already working well at 70.3% F1)
LEGAL_THRESHOLDS = DomainThresholds(domain = Domain.LEGAL,
structural = MetricThresholds(synthetic_threshold = 0.63, authentic_threshold = 0.33, weight = 0.13, confidence_multiplier = 1.0),
perplexity = MetricThresholds(synthetic_threshold = 0.53, authentic_threshold = 0.37, weight = 0.24, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.47, authentic_threshold = 0.41, weight = 0.15, confidence_multiplier = 1.0),
semantic = MetricThresholds(synthetic_threshold = 0.63, authentic_threshold = 0.33, weight = 0.14, confidence_multiplier = 1.0),
linguistic = MetricThresholds(synthetic_threshold = 0.66, authentic_threshold = 0.30, weight = 0.10, confidence_multiplier = 0.95),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.69, authentic_threshold = 0.27, weight = 0.24, confidence_multiplier = 1.0),
ensemble_threshold = 0.35,
)
# MEDICAL
MEDICAL_THRESHOLDS = DomainThresholds(domain = Domain.MEDICAL,
structural = MetricThresholds(synthetic_threshold = 0.57, authentic_threshold = 0.34, weight = 0.13, confidence_multiplier = 0.90),
perplexity = MetricThresholds(synthetic_threshold = 0.48, authentic_threshold = 0.38, weight = 0.23, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.43, authentic_threshold = 0.42, weight = 0.14, confidence_multiplier = 0.90),
semantic = MetricThresholds(synthetic_threshold = 0.57, authentic_threshold = 0.34, weight = 0.14, confidence_multiplier = 0.95),
linguistic = MetricThresholds(synthetic_threshold = 0.60, authentic_threshold = 0.31, weight = 0.11, confidence_multiplier = 0.90),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.63, authentic_threshold = 0.28, weight = 0.25, confidence_multiplier = 0.95),
ensemble_threshold = 0.29,
)
# JOURNALISM
JOURNALISM_THRESHOLDS = DomainThresholds(domain = Domain.JOURNALISM,
structural = MetricThresholds(synthetic_threshold = 0.59, authentic_threshold = 0.33, weight = 0.15, confidence_multiplier = 1.0),
perplexity = MetricThresholds(synthetic_threshold = 0.55, authentic_threshold = 0.37, weight = 0.22, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.51, authentic_threshold = 0.41, weight = 0.15, confidence_multiplier = 1.0),
semantic = MetricThresholds(synthetic_threshold = 0.59, authentic_threshold = 0.33, weight = 0.13, confidence_multiplier = 1.0),
linguistic = MetricThresholds(synthetic_threshold = 0.61, authentic_threshold = 0.31, weight = 0.10, confidence_multiplier = 0.95),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.65, authentic_threshold = 0.27, weight = 0.25, confidence_multiplier = 0.95),
ensemble_threshold = 0.32,
)
# MARKETING (marketing copy can be naturally formulaic)
MARKETING_THRESHOLDS = DomainThresholds(domain = Domain.MARKETING,
structural = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.35, weight = 0.16, confidence_multiplier = 1.0),
perplexity = MetricThresholds(synthetic_threshold = 0.49, authentic_threshold = 0.38, weight = 0.22, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.40, authentic_threshold = 0.42, weight = 0.17, confidence_multiplier = 1.0),
semantic = MetricThresholds(synthetic_threshold = 0.40, authentic_threshold = 0.35, weight = 0.14, confidence_multiplier = 0.95),
linguistic = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.32, weight = 0.10, confidence_multiplier = 0.90),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.55, authentic_threshold = 0.28, weight = 0.21, confidence_multiplier = 0.95),
ensemble_threshold = 0.30,
)
# SOCIAL MEDIA (social media is naturally chaotic and unstable)
SOCIAL_MEDIA_THRESHOLDS = DomainThresholds(domain = Domain.SOCIAL_MEDIA,
structural = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.37, weight = 0.16, confidence_multiplier = 0.85),
perplexity = MetricThresholds(synthetic_threshold = 0.48, authentic_threshold = 0.39, weight = 0.23, confidence_multiplier = 0.90),
entropy = MetricThresholds(synthetic_threshold = 0.48, authentic_threshold = 0.43, weight = 0.20, confidence_multiplier = 0.90),
semantic = MetricThresholds(synthetic_threshold = 0.50, authentic_threshold = 0.37, weight = 0.14, confidence_multiplier = 0.85),
linguistic = MetricThresholds(synthetic_threshold = 0.52, authentic_threshold = 0.34, weight = 0.09, confidence_multiplier = 0.80),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.54, authentic_threshold = 0.29, weight = 0.18, confidence_multiplier = 0.85),
ensemble_threshold = 0.30,
)
# PERSONAL BLOG
BLOG_PERSONAL_THRESHOLDS = DomainThresholds(domain = Domain.BLOG_PERSONAL,
structural = MetricThresholds(synthetic_threshold = 0.51, authentic_threshold = 0.40, weight = 0.16, confidence_multiplier = 1.0),
perplexity = MetricThresholds(synthetic_threshold = 0.52, authentic_threshold = 0.43, weight = 0.21, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.48, authentic_threshold = 0.47, weight = 0.17, confidence_multiplier = 1.0),
semantic = MetricThresholds(synthetic_threshold = 0.51, authentic_threshold = 0.40, weight = 0.14, confidence_multiplier = 1.0),
linguistic = MetricThresholds(synthetic_threshold = 0.54, authentic_threshold = 0.37, weight = 0.10, confidence_multiplier = 0.95),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.57, authentic_threshold = 0.34, weight = 0.22, confidence_multiplier = 0.95),
ensemble_threshold = 0.32,
)
# TUTORIAL/HOW-TO (Tutorials follow patterns)
TUTORIAL_THRESHOLDS = DomainThresholds(domain = Domain.TUTORIAL,
structural = MetricThresholds(synthetic_threshold = 0.55, authentic_threshold = 0.37, weight = 0.14, confidence_multiplier = 0.95),
perplexity = MetricThresholds(synthetic_threshold = 0.47, authentic_threshold = 0.40, weight = 0.23, confidence_multiplier = 1.0),
entropy = MetricThresholds(synthetic_threshold = 0.45, authentic_threshold = 0.44, weight = 0.15, confidence_multiplier = 0.95),
semantic = MetricThresholds(synthetic_threshold = 0.53, authentic_threshold = 0.37, weight = 0.14, confidence_multiplier = 1.0),
linguistic = MetricThresholds(synthetic_threshold = 0.56, authentic_threshold = 0.34, weight = 0.10, confidence_multiplier = 0.90),
multi_perturbation_stability = MetricThresholds(synthetic_threshold = 0.60, authentic_threshold = 0.31, weight = 0.24, confidence_multiplier = 0.95),
ensemble_threshold = 0.31,
)
# THRESHOLD REGISTRY
THRESHOLD_REGISTRY : Dict[Domain, DomainThresholds] = {Domain.GENERAL : DEFAULT_THRESHOLDS,
Domain.ACADEMIC : ACADEMIC_THRESHOLDS,
Domain.CREATIVE : CREATIVE_THRESHOLDS,
Domain.AI_ML : AI_ML_THRESHOLDS,
Domain.SOFTWARE_DEV : SOFTWARE_DEV_THRESHOLDS,
Domain.TECHNICAL_DOC : TECHNICAL_DOC_THRESHOLDS,
Domain.ENGINEERING : ENGINEERING_THRESHOLDS,
Domain.SCIENCE : SCIENCE_THRESHOLDS,
Domain.BUSINESS : BUSINESS_THRESHOLDS,
Domain.LEGAL : LEGAL_THRESHOLDS,
Domain.MEDICAL : MEDICAL_THRESHOLDS,
Domain.JOURNALISM : JOURNALISM_THRESHOLDS,
Domain.MARKETING : MARKETING_THRESHOLDS,
Domain.SOCIAL_MEDIA : SOCIAL_MEDIA_THRESHOLDS,
Domain.BLOG_PERSONAL : BLOG_PERSONAL_THRESHOLDS,
Domain.TUTORIAL : TUTORIAL_THRESHOLDS,
}
# CONFIDENCE LEVEL MAPPING
CONFIDENCE_RANGES : Dict[ConfidenceLevel, Tuple[float, float]] = {ConfidenceLevel.VERY_LOW : (0.0, 0.4),
ConfidenceLevel.LOW : (0.4, 0.6),
ConfidenceLevel.MEDIUM : (0.6, 0.75),
ConfidenceLevel.HIGH : (0.75, 0.9),
ConfidenceLevel.VERY_HIGH : (0.9, 1.0),
}
# HELPER FUNCTIONS
def get_threshold_for_domain(domain: Domain) -> DomainThresholds:
"""
Get thresholds for a specific domain
"""
return THRESHOLD_REGISTRY.get(domain, DEFAULT_THRESHOLDS)
def get_confidence_level(score: float) -> ConfidenceLevel:
"""
Determine confidence level for authenticity estimation: score represents synthetic-likeness probability
"""
for level, (min_val, max_val) in CONFIDENCE_RANGES.items():
if (min_val <= score < max_val):
return level
return ConfidenceLevel.VERY_HIGH
def adjust_threshold_by_confidence(threshold: float, confidence: float, conservative: bool = True) -> float:
"""
Adjust threshold based on confidence level
"""
if conservative:
adjustment = min(((1 - confidence) * 0.1), 0.05)
adjusted_threshold = threshold + adjustment
return adjusted_threshold
else:
adjustment = confidence * 0.05
adjusted_threshold = threshold - adjustment
return adjusted_threshold
def interpolate_thresholds(domain1: Domain, domain2: Domain, weight1: float = 0.5) -> DomainThresholds:
"""
Interpolate between two domain thresholds
"""
thresh1 = get_threshold_for_domain(domain = domain1)
thresh2 = get_threshold_for_domain(domain = domain2)
weight2 = 1 - weight1
def interpolate_metric(m1: MetricThresholds, m2: MetricThresholds) -> MetricThresholds:
return MetricThresholds(synthetic_threshold = m1.synthetic_threshold * weight1 + m2.synthetic_threshold * weight2,
authentic_threshold = m1.authentic_threshold * weight1 + m2.authentic_threshold * weight2,
weight = m1.weight * weight1 + m2.weight * weight2,
confidence_multiplier = m1.confidence_multiplier * weight1 + m2.confidence_multiplier * weight2,
)
return DomainThresholds(domain = domain1,
structural = interpolate_metric(thresh1.structural, thresh2.structural),
perplexity = interpolate_metric(thresh1.perplexity, thresh2.perplexity),
entropy = interpolate_metric(thresh1.entropy, thresh2.entropy),
semantic = interpolate_metric(thresh1.semantic, thresh2.semantic),
linguistic = interpolate_metric(thresh1.linguistic, thresh2.linguistic),
multi_perturbation_stability = interpolate_metric(thresh1.multi_perturbation_stability, thresh2.multi_perturbation_stability),
ensemble_threshold = thresh1.ensemble_threshold * weight1 + thresh2.ensemble_threshold * weight2,
)
def get_active_metric_weights(domain: Domain, enabled_metrics: Dict[str, bool]) -> Dict[str, float]:
"""
Get weights for enabled metrics, normalized to sum to 1.0
"""
thresholds = get_threshold_for_domain(domain = domain)
metric_mapping = {"structural" : thresholds.structural,
"perplexity" : thresholds.perplexity,
"entropy" : thresholds.entropy,
"semantic" : thresholds.semantic,
"linguistic" : thresholds.linguistic,
"multi_perturbation_stability" : thresholds.multi_perturbation_stability,
}
active_weights = dict()
for metric_name, threshold_obj in metric_mapping.items():
if enabled_metrics.get(metric_name, False):
active_weights[metric_name] = threshold_obj.weight
# Normalize
total_weight = sum(active_weights.values())
if (total_weight > 0):
active_weights = {name: weight / total_weight for name, weight in active_weights.items()}
return active_weights
# Export
__all__ = ["Domain",
"ConfidenceLevel",
"MetricThresholds",
"DomainThresholds",
"CONFIDENCE_RANGES",
"DEFAULT_THRESHOLDS",
"THRESHOLD_REGISTRY",
"get_confidence_level",
"interpolate_thresholds",
"get_threshold_for_domain",
"get_active_metric_weights",
"adjust_threshold_by_confidence",
] |