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# DEPENDENCIES
import re
import numpy as np
from enum import Enum
from typing import Any
from typing import Dict
from typing import List
from typing import Tuple
from loguru import logger
from typing import Optional
from dataclasses import dataclass
from config.threshold_config import Domain
from metrics.base_metric import MetricResult
from processors.text_processor import ProcessedText
class AIModel(Enum):
"""
Supported AI models for attribution - ALIGNED WITH DOCUMENTATION
"""
GPT_3_5 = "gpt-3.5-turbo"
GPT_4 = "gpt-4"
GPT_4_TURBO = "gpt-4-turbo"
GPT_4o = "gpt-4o"
CLAUDE_3_OPUS = "claude-3-opus"
CLAUDE_3_SONNET = "claude-3-sonnet"
CLAUDE_3_HAIKU = "claude-3-haiku"
GEMINI_PRO = "gemini-pro"
GEMINI_ULTRA = "gemini-ultra"
GEMINI_FLASH = "gemini-flash"
LLAMA_2 = "llama-2"
LLAMA_3 = "llama-3"
MISTRAL = "mistral"
MIXTRAL = "mixtral"
DEEPSEEK_CHAT = "deepseek-chat"
DEEPSEEK_CODER = "deepseek-coder"
HUMAN = "human"
UNKNOWN = "unknown"
@dataclass
class AttributionResult:
"""
Result of AI model attribution
"""
predicted_model : AIModel
confidence : float
model_probabilities : Dict[str, float]
reasoning : List[str]
fingerprint_matches : Dict[str, int]
domain_used : Domain
metric_contributions: Dict[str, float]
def to_dict(self) -> Dict[str, Any]:
"""
Convert to dictionary
"""
return {"predicted_model" : self.predicted_model.value,
"confidence" : round(self.confidence, 4),
"model_probabilities" : {model: round(prob, 4) for model, prob in self.model_probabilities.items()},
"reasoning" : self.reasoning,
"fingerprint_matches" : self.fingerprint_matches,
"domain_used" : self.domain_used.value,
"metric_contributions": {metric: round(contrib, 4) for metric, contrib in self.metric_contributions.items()},
}
class ModelAttributor:
"""
Model attribution
FEATURES:
- Domain-aware calibration
- 6-metric ensemble integration
- Confidence-weighted aggregation
- Explainable reasoning
"""
# Metric weights from technical specification
METRIC_WEIGHTS = {"perplexity" : 0.25,
"structural" : 0.15,
"semantic_analysis" : 0.15,
"entropy" : 0.20,
"linguistic" : 0.15,
"multi_perturbation_stability" : 0.10,
}
# Domain-aware model patterns for ALL 16 DOMAINS
DOMAIN_MODEL_PREFERENCES = {Domain.GENERAL : [AIModel.GPT_4, AIModel.CLAUDE_3_SONNET, AIModel.GEMINI_PRO, AIModel.GPT_3_5],
Domain.ACADEMIC : [AIModel.GPT_4, AIModel.CLAUDE_3_OPUS, AIModel.GEMINI_ULTRA, AIModel.GPT_4_TURBO],
Domain.TECHNICAL_DOC : [AIModel.GPT_4_TURBO, AIModel.CLAUDE_3_SONNET, AIModel.LLAMA_3, AIModel.GPT_4],
Domain.AI_ML : [AIModel.GPT_4_TURBO, AIModel.GPT_4, AIModel.CLAUDE_3_OPUS, AIModel.DEEPSEEK_CODER],
Domain.SOFTWARE_DEV : [AIModel.GPT_4_TURBO, AIModel.DEEPSEEK_CODER, AIModel.CLAUDE_3_SONNET, AIModel.GPT_4],
Domain.ENGINEERING : [AIModel.GPT_4, AIModel.CLAUDE_3_OPUS, AIModel.GPT_4_TURBO, AIModel.LLAMA_3],
Domain.SCIENCE : [AIModel.GPT_4, AIModel.CLAUDE_3_OPUS, AIModel.GEMINI_ULTRA, AIModel.GPT_4_TURBO],
Domain.BUSINESS : [AIModel.GPT_4, AIModel.CLAUDE_3_SONNET, AIModel.GEMINI_PRO, AIModel.GPT_3_5],
Domain.LEGAL : [AIModel.GPT_4, AIModel.CLAUDE_3_OPUS, AIModel.GPT_4_TURBO, AIModel.CLAUDE_3_SONNET],
Domain.MEDICAL : [AIModel.GPT_4, AIModel.CLAUDE_3_OPUS, AIModel.GEMINI_ULTRA, AIModel.GPT_4_TURBO],
Domain.JOURNALISM : [AIModel.GPT_4, AIModel.CLAUDE_3_SONNET, AIModel.GEMINI_PRO, AIModel.GPT_3_5],
Domain.CREATIVE : [AIModel.CLAUDE_3_OPUS, AIModel.GPT_4, AIModel.GEMINI_PRO, AIModel.CLAUDE_3_SONNET],
Domain.MARKETING : [AIModel.GPT_4, AIModel.CLAUDE_3_SONNET, AIModel.GEMINI_PRO, AIModel.GPT_3_5],
Domain.SOCIAL_MEDIA : [AIModel.GPT_3_5, AIModel.GEMINI_PRO, AIModel.DEEPSEEK_CHAT, AIModel.LLAMA_3],
Domain.BLOG_PERSONAL : [AIModel.CLAUDE_3_SONNET, AIModel.GPT_4, AIModel.GEMINI_PRO, AIModel.GPT_3_5],
Domain.TUTORIAL : [AIModel.GPT_4, AIModel.CLAUDE_3_SONNET, AIModel.GEMINI_PRO, AIModel.GPT_4_TURBO],
}
# Model-specific fingerprints with comprehensive patterns
MODEL_FINGERPRINTS = {AIModel.GPT_3_5 : {"phrases" : ["as an ai language model",
"i don't have personal opinions",
"it's important to note that",
"it's worth noting that",
"keep in mind that",
"bear in mind that",
"i should point out",
"it's also important to",
"additionally, it's worth",
"furthermore, it should be",
"i cannot provide",
"i'm unable to",
"i don't have the ability",
"based on the information",
"according to the context",
],
"sentence_starters" : ["however,",
"additionally,",
"furthermore,",
"moreover,",
"in conclusion,",
"therefore,",
"consequently,",
"as a result,",
"in summary,",
"ultimately,",
],
"structural_patterns" : ["firstly",
"secondly",
"thirdly",
"on one hand",
"on the other hand",
"in terms of",
"with regard to",
],
"punctuation_patterns" : {"em_dash_frequency" : (0.01, 0.03),
"semicolon_frequency" : (0.005, 0.015),
"parentheses_frequency" : (0.01, 0.04),
},
"style_markers" : {"avg_sentence_length" : (18, 25),
"transition_word_density" : (0.08, 0.15),
"formality_score" : (0.7, 0.9),
"hedging_language" : (0.05, 0.12),
}
},
AIModel.GPT_4 : {"phrases" : ["it's important to note that",
"it's worth mentioning that",
"to clarify this point",
"in other words,",
"that being said,",
"in essence,",
"fundamentally,",
"at its core,",
"from a broader perspective",
"when considering",
"this suggests that",
"this implies that",
"it follows that",
"consequently,",
"accordingly,",
],
"sentence_starters" : ["interestingly,",
"notably,",
"crucially,",
"essentially,",
"ultimately,",
"significantly,",
"importantly,",
"remarkably,",
"surprisingly,",
],
"structural_patterns" : ["in light of",
"with respect to",
"pertaining to",
"as evidenced by",
"as indicated by",
"as suggested by",
],
"punctuation_patterns" : {"em_dash_frequency" : (0.02, 0.05),
"colon_frequency" : (0.01, 0.03),
"semicolon_frequency" : (0.01, 0.02),
},
"style_markers" : {"avg_sentence_length" : (20, 28),
"vocabulary_sophistication" : (0.7, 0.9),
"conceptual_density" : (0.6, 0.85),
"analytical_depth" : (0.65, 0.9),
}
},
AIModel.CLAUDE_3_OPUS : {"phrases" : ["i'd be glad to",
"i'm happy to help",
"let me explain this",
"to clarify this further",
"in this context,",
"from this perspective,",
"building on that point",
"expanding on this idea",
"delving deeper into",
"to elaborate further",
"it's worth considering",
"this raises the question",
"this highlights the importance",
"this underscores the need",
],
"sentence_starters" : ["certainly,",
"indeed,",
"particularly,",
"specifically,",
"notably,",
"importantly,",
"interestingly,",
"crucially,",
],
"structural_patterns" : ["in other words",
"to put it differently",
"that is to say",
"for instance",
"for example",
"as an illustration",
],
"punctuation_patterns" : {"em_dash_frequency" : (0.015, 0.04),
"parenthetical_usage" : (0.02, 0.06),
"colon_frequency" : (0.008, 0.025),
},
"style_markers" : {"avg_sentence_length" : (17, 24),
"nuanced_language" : (0.6, 0.85),
"explanatory_depth" : (0.7, 0.95),
"conceptual_clarity" : (0.65, 0.9),
}
},
AIModel.GEMINI_PRO : {"phrases" : ["here's what you need to know",
"here's how it works",
"let's explore this",
"let's look at this",
"consider this example",
"think of it this way",
"imagine if you will",
"picture this scenario",
"to break it down",
"in simple terms",
"put simply,",
"basically,",
"the key point is",
"the main idea here",
],
"sentence_starters" : ["now,",
"so,",
"well,",
"basically,",
"essentially,",
"actually,",
"technically,",
"practically,",
],
"structural_patterns" : ["on that note",
"speaking of which",
"by the way",
"as a side note",
"incidentally",
"in any case",
],
"punctuation_patterns" : {"exclamation_frequency" : (0.01, 0.03),
"question_frequency" : (0.02, 0.05),
"ellipsis_frequency" : (0.005, 0.02),
},
"style_markers" : {"avg_sentence_length" : (15, 22),
"conversational_tone" : (0.5, 0.8),
"accessibility_score" : (0.6, 0.9),
"engagement_level" : (0.55, 0.85),
}
},
AIModel.LLAMA_3 : {"phrases" : ["it's worth noting",
"it's important to understand",
"this means that",
"this indicates that",
"this shows that",
"this demonstrates that",
"based on this,",
"given this context",
"in this case,",
"for this reason",
"as such,",
"therefore,",
],
"sentence_starters" : ["first,",
"second,",
"third,",
"next,",
"then,",
"finally,",
"overall,",
"in general,",
],
"structural_patterns" : ["in addition",
"moreover",
"furthermore",
"however",
"nevertheless",
"nonetheless",
],
"punctuation_patterns" : {"comma_frequency" : (0.08, 0.15),
"period_frequency" : (0.06, 0.12),
"conjunction_frequency" : (0.05, 0.1),
},
"style_markers" : {"avg_sentence_length" : (16, 23),
"directness_score" : (0.6, 0.85),
"clarity_score" : (0.65, 0.9),
"structural_consistency" : (0.7, 0.95),
}
},
AIModel.DEEPSEEK_CHAT : {"phrases" : ["i understand you're asking",
"let me help you with that",
"i can assist you with",
"regarding your question",
"to answer your question",
"in response to your query",
"based on your request",
"as per your question",
"concerning your inquiry",
"with respect to your question",
"i'll do my best to",
"i'll try to help you",
"allow me to explain",
"let me break it down",
],
"sentence_starters" : ["well,",
"okay,",
"so,",
"now,",
"first,",
"actually,",
"specifically,",
"generally,",
],
"structural_patterns" : ["in other words",
"to put it simply",
"that is",
"for example",
"for instance",
"such as",
],
"punctuation_patterns" : {"comma_frequency" : (0.07, 0.14),
"period_frequency" : (0.05, 0.11),
"question_frequency" : (0.01, 0.04),
},
"style_markers" : {"avg_sentence_length" : (14, 21),
"helpfulness_tone" : (0.6, 0.9),
"explanatory_style" : (0.55, 0.85),
"user_focus" : (0.65, 0.95),
}
},
AIModel.MIXTRAL : {"phrases" : ["it should be noted that",
"it is important to recognize",
"this suggests that",
"this implies that",
"this indicates that",
"from this we can see",
"based on this analysis",
"considering these points",
"taking into account",
"in light of these factors",
],
"sentence_starters" : ["however,",
"moreover,",
"furthermore,",
"additionally,",
"conversely,",
"similarly,",
"likewise,",
],
"structural_patterns" : ["on the one hand",
"on the other hand",
"in contrast",
"by comparison",
"as opposed to",
"rather than",
],
"punctuation_patterns" : {"semicolon_frequency" : (0.008, 0.02),
"colon_frequency" : (0.006, 0.018),
"parentheses_frequency" : (0.012, 0.035),
},
"style_markers" : {"avg_sentence_length" : (19, 26),
"analytical_tone" : (0.65, 0.9),
"comparative_language" : (0.5, 0.8),
"balanced_perspective" : (0.6, 0.85),
}
}
}
def __init__(self):
"""
Initialize model attributor with domain awareness
"""
self.is_initialized = False
logger.info("ModelAttributor initialized with domain-aware calibration")
def initialize(self) -> bool:
"""
Initialize attribution system
"""
try:
self.is_initialized = True
logger.success("Model attribution system initialized with metric ensemble")
return True
except Exception as e:
logger.error(f"Failed to initialize attribution system: {repr(e)}")
return False
def attribute(self, text: str, processed_text: Optional[ProcessedText] = None, metric_results: Optional[Dict[str, MetricResult]] = None,
domain: Domain = Domain.GENERAL) -> AttributionResult:
"""
Attribute text to specific AI model with domain awareness
Arguments:
----------
text { str } : Input text
processed_text { ProcessedText } : Processed text metadata
metric_results { dict } : Results from 6 core metrics
domain { Domain } : Text domain for calibration
Returns:
--------
{ AttributionResult } : Attribution result with domain context
"""
try:
# Get domain-specific model preferences
domain_preferences = self.DOMAIN_MODEL_PREFERENCES.get(domain, [AIModel.GPT_4, AIModel.CLAUDE_3_SONNET])
# Fingerprint analysis
fingerprint_scores = self._calculate_fingerprint_scores(text = text,
domain = domain,
)
# Statistical pattern analysis
statistical_scores = self._analyze_statistical_patterns(text = text,
domain = domain,
)
# Metric-based attribution using all 6 metrics
metric_scores = self._analyze_metric_patterns(metric_results = metric_results, domain = domain) if metric_results else {}
# Ensemble Combination
combined_scores, metric_contributions = self._combine_attribution_scores(fingerprint_scores = fingerprint_scores,
statistical_scores = statistical_scores,
metric_scores = metric_scores,
domain = domain,
)
# Domain-aware prediction : Always show the actual highest probability model
predicted_model, confidence = self._make_domain_aware_prediction(combined_scores = combined_scores,
domain = domain,
domain_preferences = domain_preferences,
)
# Reasoning with domain context
reasoning = self._generate_detailed_reasoning(predicted_model = predicted_model,
confidence = confidence,
domain = domain,
metric_contributions = metric_contributions,
combined_scores = combined_scores,
)
return AttributionResult(predicted_model = predicted_model,
confidence = confidence,
model_probabilities = combined_scores,
reasoning = reasoning,
fingerprint_matches = self._get_top_fingerprints(fingerprint_scores),
domain_used = domain,
metric_contributions = metric_contributions,
)
except Exception as e:
logger.error(f"Error in model attribution: {repr(e)}")
return self._create_unknown_result(domain)
def _calculate_fingerprint_scores(self, text: str, domain: Domain) -> Dict[AIModel, float]:
"""
Calculate fingerprint match scores with domain calibration - for all domains
"""
scores = {model: 0.0 for model in AIModel if model not in [AIModel.HUMAN, AIModel.UNKNOWN]}
# Adjust sensitivity based on all domains
domain_sensitivity = {Domain.GENERAL : 1.00,
Domain.ACADEMIC : 1.20,
Domain.CREATIVE : 0.90,
Domain.AI_ML : 1.15,
Domain.SOFTWARE_DEV : 1.15,
Domain.TECHNICAL_DOC : 1.10,
Domain.ENGINEERING : 1.10,
Domain.SCIENCE : 1.20,
Domain.BUSINESS : 1.05,
Domain.LEGAL : 1.25,
Domain.MEDICAL : 1.20,
Domain.JOURNALISM : 1.00,
Domain.MARKETING : 0.95,
Domain.SOCIAL_MEDIA : 0.80,
Domain.BLOG_PERSONAL : 0.90,
Domain.TUTORIAL : 1.00,
}
sensitivity = domain_sensitivity.get(domain, 1.0)
text_lower = text.lower()
for model, fingerprints in self.MODEL_FINGERPRINTS.items():
match_count = 0
total_checks = 0
# Check phrase matches
if ("phrases" in fingerprints):
for phrase in fingerprints["phrases"]:
if (phrase in text_lower):
match_count += 3
total_checks += 1
# Check sentence starters
if ("sentence_starters" in fingerprints):
sentences = re.split(r'[.!?]+', text)
for sentence in sentences:
sentence = sentence.strip().lower()
for starter in fingerprints["sentence_starters"]:
if (sentence.startswith(starter)):
match_count += 2
break
total_checks += len(sentences)
# Check structural patterns
if ("structural_patterns" in fingerprints):
for pattern in fingerprints["structural_patterns"]:
if (pattern in text_lower):
match_count += 2
total_checks += 1
# Calculate normalized score
if (total_checks > 0):
base_score = min(1.0, match_count / (total_checks * 0.5))
# Apply domain calibration
scores[model] = min(1.0, base_score * sensitivity)
return scores
def _analyze_statistical_patterns(self, text: str, domain: Domain) -> Dict[AIModel, float]:
"""
Analyze statistical patterns to identify model with domain awareness
"""
scores = {model: 0.3 for model in AIModel if model not in [AIModel.HUMAN, AIModel.UNKNOWN]}
# Calculate text statistics
sentences = re.split(r'[.!?]+', text)
sentences = [s.strip() for s in sentences if s.strip()]
words = text.split()
if not sentences or not words:
return scores
# Basic statistics
avg_sentence_length = len(words) / len(sentences)
word_count = len(words)
sentence_count = len(sentences)
# Punctuation frequencies
em_dash_freq = text.count('—') / word_count if word_count else 0
semicolon_freq = text.count(';') / word_count if word_count else 0
colon_freq = text.count(':') / word_count if word_count else 0
comma_freq = text.count(',') / word_count if word_count else 0
question_freq = text.count('?') / sentence_count if sentence_count else 0
exclamation_freq = text.count('!') / sentence_count if sentence_count else 0
# DOMAIN-AWARE: Adjust expectations based on domains
domain_adjustments = {Domain.GENERAL : 1.00,
Domain.ACADEMIC : 1.10,
Domain.CREATIVE : 0.95,
Domain.AI_ML : 1.05,
Domain.SOFTWARE_DEV : 1.05,
Domain.TECHNICAL_DOC : 1.05,
Domain.ENGINEERING : 1.05,
Domain.SCIENCE : 1.08,
Domain.BUSINESS : 1.00,
Domain.LEGAL : 1.12,
Domain.MEDICAL : 1.08,
Domain.JOURNALISM : 0.95,
Domain.MARKETING : 0.92,
Domain.SOCIAL_MEDIA : 0.85,
Domain.BLOG_PERSONAL : 0.95,
Domain.TUTORIAL : 1.00,
}
domain_factor = domain_adjustments.get(domain, 1.0)
# Compare against model fingerprints
for model, fingerprints in self.MODEL_FINGERPRINTS.items():
if ("style_markers" not in fingerprints) or ("punctuation_patterns" not in fingerprints):
continue
style = fingerprints["style_markers"]
punct = fingerprints["punctuation_patterns"]
match_score = 0.3
# Check sentence length with domain adjustment
if ("avg_sentence_length" in style):
min_len, max_len = style["avg_sentence_length"]
adjusted_min = min_len * domain_factor
adjusted_max = max_len * domain_factor
if (adjusted_min <= avg_sentence_length <= adjusted_max):
match_score += 0.25
# Check punctuation patterns
punctuation_checks = [("em_dash_frequency", em_dash_freq),
("semicolon_frequency", semicolon_freq),
("colon_frequency", colon_freq),
("comma_frequency", comma_freq),
("question_frequency", question_freq),
("exclamation_frequency", exclamation_freq),
]
for pattern_name, observed_freq in punctuation_checks:
if (pattern_name in punct):
min_freq, max_freq = punct[pattern_name]
if (min_freq <= observed_freq <= max_freq):
match_score += 0.08
scores[model] = min(1.0, match_score)
return scores
def _analyze_metric_patterns(self, metric_results: Dict[str, MetricResult], domain: Domain) -> Dict[AIModel, float]:
"""
Use all 6 metrics with proper weights for attribution
"""
scores = {model: 0.0 for model in AIModel if model not in [AIModel.HUMAN, AIModel.UNKNOWN]}
if not metric_results:
return scores
# DOMAIN-AWARE: Adjust metric sensitivity based on domain
domain_metric_weights = {Domain.GENERAL : {"perplexity": 1.0, "structural": 1.0, "entropy": 1.0, "semantic_analysis": 1.0, "linguistic": 1.0, "multi_perturbation_stability": 1.0},
Domain.ACADEMIC : {"perplexity": 1.2, "structural": 1.0, "entropy": 0.9, "semantic_analysis": 1.1, "linguistic": 1.3, "multi_perturbation_stability": 0.8},
Domain.TECHNICAL_DOC : {"perplexity": 1.2, "structural": 1.1, "entropy": 0.9, "semantic_analysis": 1.2, "linguistic": 1.1, "multi_perturbation_stability": 0.8},
Domain.AI_ML : {"perplexity": 1.3, "structural": 1.0, "entropy": 0.9, "semantic_analysis": 1.2, "linguistic": 1.2, "multi_perturbation_stability": 0.8},
Domain.SOFTWARE_DEV : {"perplexity": 1.2, "structural": 1.1, "entropy": 0.9, "semantic_analysis": 1.1, "linguistic": 1.0, "multi_perturbation_stability": 0.9},
Domain.ENGINEERING : {"perplexity": 1.2, "structural": 1.1, "entropy": 0.9, "semantic_analysis": 1.1, "linguistic": 1.2, "multi_perturbation_stability": 0.8},
Domain.SCIENCE : {"perplexity": 1.2, "structural": 1.0, "entropy": 0.9, "semantic_analysis": 1.2, "linguistic": 1.3, "multi_perturbation_stability": 0.8},
Domain.BUSINESS : {"perplexity": 1.1, "structural": 1.0, "entropy": 1.0, "semantic_analysis": 1.2, "linguistic": 1.1, "multi_perturbation_stability": 0.9},
Domain.LEGAL : {"perplexity": 1.2, "structural": 1.1, "entropy": 0.9, "semantic_analysis": 1.3, "linguistic": 1.3, "multi_perturbation_stability": 0.8},
Domain.MEDICAL : {"perplexity": 1.2, "structural": 1.0, "entropy": 0.9, "semantic_analysis": 1.2, "linguistic": 1.2, "multi_perturbation_stability": 0.8},
Domain.JOURNALISM : {"perplexity": 1.1, "structural": 1.0, "entropy": 1.0, "semantic_analysis": 1.1, "linguistic": 1.1, "multi_perturbation_stability": 0.9},
Domain.CREATIVE : {"perplexity": 0.9, "structural": 0.9, "entropy": 1.2, "semantic_analysis": 1.0, "linguistic": 1.3, "multi_perturbation_stability": 0.9},
Domain.MARKETING : {"perplexity": 1.0, "structural": 1.0, "entropy": 1.1, "semantic_analysis": 1.1, "linguistic": 1.2, "multi_perturbation_stability": 0.8},
Domain.SOCIAL_MEDIA : {"perplexity": 1.0, "structural": 0.8, "entropy": 1.3, "semantic_analysis": 0.9, "linguistic": 0.9, "multi_perturbation_stability": 0.9},
Domain.BLOG_PERSONAL : {"perplexity": 1.0, "structural": 0.9, "entropy": 1.2, "semantic_analysis": 1.0, "linguistic": 1.1, "multi_perturbation_stability": 0.8},
Domain.TUTORIAL : {"perplexity": 1.1, "structural": 1.0, "entropy": 1.0, "semantic_analysis": 1.1, "linguistic": 1.1, "multi_perturbation_stability": 0.9},
}
domain_weights = domain_metric_weights.get(domain, domain_metric_weights[Domain.GENERAL])
# PERPLEXITY ANALYSIS (25% weight)
if ("perplexity" in metric_results):
perplexity_result = metric_results["perplexity"]
overall_perplexity = perplexity_result.details.get("overall_perplexity", 50)
domain_weight = domain_weights.get("perplexity", 1.0)
# GPT models typically have lower perplexity
if (overall_perplexity < 25):
scores[AIModel.GPT_4] += 0.6 * self.METRIC_WEIGHTS["perplexity"] * domain_weight
scores[AIModel.GPT_4_TURBO] += 0.5 * self.METRIC_WEIGHTS["perplexity"] * domain_weight
elif (overall_perplexity < 35):
scores[AIModel.GPT_3_5] += 0.4 * self.METRIC_WEIGHTS["perplexity"] * domain_weight
scores[AIModel.GEMINI_PRO] += 0.3 * self.METRIC_WEIGHTS["perplexity"] * domain_weight
# STRUCTURAL ANALYSIS (15% weight)
if ("structural" in metric_results):
structural_result = metric_results["structural"]
burstiness = structural_result.details.get("burstiness_score", 0.5)
uniformity = structural_result.details.get("length_uniformity", 0.5)
domain_weight = domain_weights.get("structural", 1.0)
# Claude models show more structural consistency
if (uniformity > 0.7):
scores[AIModel.CLAUDE_3_OPUS] += 0.5 * self.METRIC_WEIGHTS["structural"] * domain_weight
scores[AIModel.CLAUDE_3_SONNET] += 0.4 * self.METRIC_WEIGHTS["structural"] * domain_weight
# SEMANTIC ANALYSIS (15% weight)
if ("semantic_analysis" in metric_results):
semantic_result = metric_results["semantic_analysis"]
coherence = semantic_result.details.get("coherence_score", 0.5)
consistency = semantic_result.details.get("consistency_score", 0.5)
domain_weight = domain_weights.get("semantic_analysis", 1.0)
# GPT-4 shows exceptional semantic coherence
if (coherence > 0.8):
scores[AIModel.GPT_4] += 0.7 * self.METRIC_WEIGHTS["semantic_analysis"] * domain_weight
scores[AIModel.GPT_4_TURBO] += 0.6 * self.METRIC_WEIGHTS["semantic_analysis"] * domain_weight
# ENTROPY ANALYSIS (20% weight)
if ("entropy" in metric_results):
entropy_result = metric_results["entropy"]
token_diversity = entropy_result.details.get("token_diversity", 0.5)
sequence_unpredictability = entropy_result.details.get("sequence_unpredictability", 0.5)
domain_weight = domain_weights.get("entropy", 1.0)
# Higher entropy diversity suggests more sophisticated models
if (token_diversity > 0.7):
scores[AIModel.CLAUDE_3_OPUS] += 0.6 * self.METRIC_WEIGHTS["entropy"] * domain_weight
scores[AIModel.GPT_4] += 0.5 * self.METRIC_WEIGHTS["entropy"] * domain_weight
# LINGUISTIC ANALYSIS (15% weight)
if ("linguistic" in metric_results):
linguistic_result = metric_results["linguistic"]
pos_diversity = linguistic_result.details.get("pos_diversity", 0.5)
syntactic_complexity = linguistic_result.details.get("syntactic_complexity", 2.5)
domain_weight = domain_weights.get("linguistic", 1.0)
# Complex linguistic patterns suggest advanced models
if (syntactic_complexity > 3.0):
scores[AIModel.CLAUDE_3_OPUS] += 0.5 * self.METRIC_WEIGHTS["linguistic"] * domain_weight
scores[AIModel.GPT_4] += 0.4 * self.METRIC_WEIGHTS["linguistic"] * domain_weight
# MULTI-PERTURBATION STABILITY ANALYSIS (10% weight)
if ("multi_perturbation_stability" in metric_results):
multi_perturbation_stability_result = metric_results["multi_perturbation_stability"]
stability = multi_perturbation_stability_result.details.get("stability_score", 0.5)
curvature = multi_perturbation_stability_result.details.get("curvature_score", 0.5)
# Specific stability patterns for different model families
if (0.4 <= stability <= 0.6):
scores[AIModel.MIXTRAL] += 0.4 * self.METRIC_WEIGHTS["multi_perturbation_stability"]
scores[AIModel.LLAMA_3] += 0.3 * self.METRIC_WEIGHTS["multi_perturbation_stability"]
# Normalize scores
for model in scores:
scores[model] = min(1.0, scores[model])
return scores
def _combine_attribution_scores(self, fingerprint_scores: Dict[AIModel, float], statistical_scores: Dict[AIModel, float],
metric_scores: Dict[AIModel, float], domain: Domain) -> Tuple[Dict[str, float], Dict[str, float]]:
"""
ENSEMBLE COMBINATION using document-specified weights and domain awareness
"""
# DOMAIN-AWARE weighting for ALL 16 DOMAINS
domain_weights = {Domain.GENERAL : {"fingerprint": 0.35, "statistical": 0.30, "metric": 0.35},
Domain.ACADEMIC : {"fingerprint": 0.30, "statistical": 0.35, "metric": 0.35},
Domain.TECHNICAL_DOC : {"fingerprint": 0.25, "statistical": 0.40, "metric": 0.35},
Domain.AI_ML : {"fingerprint": 0.28, "statistical": 0.37, "metric": 0.35},
Domain.SOFTWARE_DEV : {"fingerprint": 0.27, "statistical": 0.38, "metric": 0.35},
Domain.ENGINEERING : {"fingerprint": 0.28, "statistical": 0.37, "metric": 0.35},
Domain.SCIENCE : {"fingerprint": 0.30, "statistical": 0.35, "metric": 0.35},
Domain.BUSINESS : {"fingerprint": 0.33, "statistical": 0.35, "metric": 0.32},
Domain.LEGAL : {"fingerprint": 0.28, "statistical": 0.40, "metric": 0.32},
Domain.MEDICAL : {"fingerprint": 0.30, "statistical": 0.38, "metric": 0.32},
Domain.JOURNALISM : {"fingerprint": 0.35, "statistical": 0.33, "metric": 0.32},
Domain.CREATIVE : {"fingerprint": 0.40, "statistical": 0.30, "metric": 0.30},
Domain.MARKETING : {"fingerprint": 0.38, "statistical": 0.32, "metric": 0.30},
Domain.SOCIAL_MEDIA : {"fingerprint": 0.45, "statistical": 0.35, "metric": 0.20},
Domain.BLOG_PERSONAL : {"fingerprint": 0.42, "statistical": 0.32, "metric": 0.26},
Domain.TUTORIAL : {"fingerprint": 0.33, "statistical": 0.35, "metric": 0.32},
}
weights = domain_weights.get(domain, domain_weights[Domain.GENERAL])
combined = dict()
metric_contributions = dict()
all_models = set(fingerprint_scores.keys()) | set(statistical_scores.keys()) | set(metric_scores.keys())
for model in all_models:
score = (fingerprint_scores.get(model, 0.0) * weights["fingerprint"] +
statistical_scores.get(model, 0.0) * weights["statistical"] +
metric_scores.get(model, 0.0) * weights["metric"]
)
combined[model.value] = score
# Normalize scores to sum to 1.0 for proper probability distribution
total_score = sum(combined.values())
if (total_score > 0):
combined = {model: score / total_score for model, score in combined.items()}
# Calculate metric contributions for explainability
if metric_scores:
total_metric_impact = sum(metric_scores.values())
if (total_metric_impact > 0):
for model, score in metric_scores.items():
metric_contributions[model.value] = score / total_metric_impact
return combined, metric_contributions
def _make_domain_aware_prediction(self, combined_scores: Dict[str, float], domain: Domain, domain_preferences: List[AIModel]) -> Tuple[AIModel, float]:
"""
Domain aware prediction that considers domain-specific model preferences
"""
if not combined_scores:
return AIModel.UNKNOWN, 0.0
# Find the model with the highest probability
sorted_models = sorted(combined_scores.items(), key=lambda x: x[1], reverse=True)
if not sorted_models:
return AIModel.UNKNOWN, 0.0
best_model_name, best_score = sorted_models[0]
# Thresholding to show model only if confidence is sufficient
if (best_score < 0.01):
return AIModel.UNKNOWN, best_score
try:
best_model = AIModel(best_model_name)
except ValueError:
best_model = AIModel.UNKNOWN
# Calculate confidence - be more generous
if (len(sorted_models) > 1):
second_score = sorted_models[1][1]
margin = best_score - second_score
# More generous confidence calculation
confidence = min(1.0, best_score * 0.8 + margin * 1.5)
else:
confidence = best_score * 0.9
# Always return the actual best model, never downgrade to UNKNOWN
return best_model, max(0.05, confidence)
def _generate_detailed_reasoning(self, predicted_model: AIModel, confidence: float, domain: Domain, metric_contributions: Dict[str, float],
combined_scores: Dict[str, float]) -> List[str]:
"""
Generate Explainable reasoning - ENHANCED version
"""
reasoning = []
reasoning.append("**AI Model Attribution Analysis**")
reasoning.append("")
# Show prediction with confidence
if (predicted_model == AIModel.UNKNOWN):
reasoning.append("**Most Likely**: Unable to determine with high confidence")
else:
model_name = predicted_model.value.replace("-", " ").replace("_", " ").title()
reasoning.append(f"**Predicted Model**: {model_name}")
reasoning.append(f"**Confidence**: {confidence*100:.1f}%")
reasoning.append(f"**Domain**: {domain.value.replace('_', ' ').title()}")
reasoning.append("")
# Show model probability distribution
reasoning.append("**Model Probability Distribution:**")
reasoning.append("")
if combined_scores:
sorted_models = sorted(combined_scores.items(), key = lambda x: x[1], reverse = True)
for i, (model_name, score) in enumerate(sorted_models[:6]):
# Skip very low probabilities
if (score < 0.01):
continue
display_name = model_name.replace("-", " ").replace("_", " ").title()
percentage = score * 100
# Use proper markdown formatting
reasoning.append(f"• **{display_name}**: {percentage:.1f}%")
reasoning.append("")
# Add analysis insights
reasoning.append("**Analysis Notes:**")
if (confidence < 0.3):
reasoning.append("• Low confidence attribution - text patterns are ambiguous")
reasoning.append("• May be human-written or from multiple AI sources")
else:
reasoning.append(f"• Calibrated for {domain.value.replace('_', ' ')} domain")
# Domain-specific insights
domain_insights = {Domain.ACADEMIC : "Academic writing patterns analyzed",
Domain.TECHNICAL_DOC : "Technical coherence and structure weighted",
Domain.CREATIVE : "Stylistic and linguistic diversity emphasized",
Domain.SOCIAL_MEDIA : "Casual language and engagement patterns considered",
Domain.AI_ML : "Technical terminology and analytical patterns emphasized",
Domain.SOFTWARE_DEV : "Code-like structures and technical precision weighted",
Domain.ENGINEERING : "Technical specifications and formal language analyzed",
Domain.SCIENCE : "Scientific terminology and methodological patterns considered",
Domain.BUSINESS : "Professional communication and strategic language weighted",
Domain.LEGAL : "Formal language and legal terminology emphasized",
Domain.MEDICAL : "Medical terminology and clinical language analyzed",
Domain.JOURNALISM : "News reporting style and factual presentation weighted",
Domain.MARKETING : "Persuasive language and engagement patterns considered",
Domain.BLOG_PERSONAL : "Personal voice and conversational style analyzed",
Domain.TUTORIAL : "Instructional clarity and step-by-step structure weighted",
}
insight = domain_insights.get(domain, "Multiple attribution factors analyzed")
reasoning.append(f"• {insight}")
return reasoning
def _get_top_fingerprints(self, fingerprint_scores: Dict[AIModel, float]) -> Dict[str, int]:
"""
Get top fingerprint matches for display
"""
top_matches = dict()
sorted_models = sorted(fingerprint_scores.items(), key = lambda x: x[1], reverse = True)[:5]
for model, score in sorted_models:
# Only show meaningful matches
if (score > 0.1):
top_matches[model.value] = int(score * 100)
return top_matches
def _create_unknown_result(self, domain: Domain) -> AttributionResult:
"""
Create result for unknown attribution with domain context
"""
return AttributionResult(predicted_model = AIModel.UNKNOWN,
confidence = 0.0,
model_probabilities = {},
reasoning = [f"Model attribution inconclusive for {domain.value} content. Text may be human-written or from unidentifiable model"],
fingerprint_matches = {},
domain_used = domain,
metric_contributions = {},
)
# Export
__all__ = ["AIModel",
"ModelAttributor",
"AttributionResult",
] |