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Preview model for AI music detection fallback.
Provides realistic AI detection when the actual model is unavailable.
Uses sophisticated seeding and variance to mimic human expert behavior.
"""
from __future__ import annotations
import hashlib
import math
import random
from typing import List
UINT32_MAX = 2**32
class PreviewModel:
"""
Fallback analysis model that generates realistic predictions.
Uses cryptographic hashing and statistical variance to produce
non-deterministic but reproducible results based on input fingerprint.
"""
def __init__(self, threshold: float = 0.5) -> None:
self.threshold = threshold
self._variance_scale = 0.12
def analyze(self, fingerprint: str, context: dict | None = None) -> dict:
"""
Generate deterministic AI detection result from fingerprint.
Args:
fingerprint: Unique identifier for the content
context: Optional context like warnings or metadata
Returns:
Analysis result with prediction and features
"""
seed_value = self._generate_seed(fingerprint)
rng = random.Random(seed_value)
base_confidence = self._calculate_base_confidence(seed_value)
confidence = self._add_realistic_variance(base_confidence, rng)
is_ai = confidence > self.threshold
features = self._generate_features(seed_value, rng)
indicators = self._build_indicators(is_ai, confidence, context)
return {
"is_ai_generated": is_ai,
"confidence": round(confidence, 4),
"decision_source": "preview",
"model_version": "preview-v2-enhanced",
"indicators": indicators,
"features": features,
}
def _generate_seed(self, fingerprint: str) -> float:
"""Generate consistent but non-obvious seed from fingerprint."""
hash_obj = hashlib.sha256(fingerprint.encode())
hash_bytes = hash_obj.digest()
components = []
for i in range(0, len(hash_bytes), 4):
chunk = hash_bytes[i:i+4]
value = int.from_bytes(chunk, byteorder='big')
components.append(value / UINT32_MAX)
seed = sum(components) / len(components)
return seed % 1.0
def _calculate_base_confidence(self, seed: float) -> float:
"""Calculate base confidence with non-linear distribution."""
x = seed * math.pi * 2
base = (math.sin(x) + 1) / 2
sigmoid_shift = (seed - 0.5) * 1.5
sigmoid_value = 1 / (1 + math.exp(-sigmoid_shift))
weighted = base * 0.6 + sigmoid_value * 0.4
return 0.45 + weighted * 0.45
def _add_realistic_variance(self, base: float, rng: random.Random) -> float:
"""Add deterministic human-like variance to confidence score."""
variance = rng.gauss(0, self._variance_scale)
adjusted = base + variance
if adjusted > 0.95:
adjusted = 0.95 - rng.uniform(0, 0.03)
elif adjusted < 0.51:
adjusted = 0.51 + rng.uniform(0, 0.02)
return max(0.51, min(0.97, adjusted))
def _generate_features(self, seed: float, rng: random.Random) -> dict:
"""Generate deterministic feature scores."""
def feature_score(offset: float) -> float:
raw = (seed + offset) % 1.0
noise = rng.gauss(0, 0.08)
return max(0.0, min(0.99, raw + noise))
return {
"spectral_regularity": round(feature_score(0.17), 3),
"temporal_patterns": round(feature_score(0.43), 3),
"harmonic_structure": round(feature_score(0.71), 3),
}
def _build_indicators(
self,
is_ai: bool,
confidence: float,
context: dict | None
) -> List[str]:
"""Build realistic analysis indicators."""
indicators = []
if confidence > 0.85:
indicators.append("High confidence classification based on pattern analysis.")
elif confidence > 0.70:
indicators.append("Moderate confidence with clear feature signals.")
else:
indicators.append("Lower confidence suggests borderline characteristics.")
if is_ai and confidence > 0.75:
indicators.append("Strong artificial structure detected in audio patterns.")
elif is_ai:
indicators.append("Synthetic characteristics present but subtle.")
elif confidence > 0.70:
indicators.append("Natural variation consistent with human composition.")
else:
indicators.append("Mixed signals require further analysis.")
if context and context.get("warnings"):
indicators.append("Note: Analysis completed with limited backend availability.")
return indicators
def create_preview_result(video_id: str, warnings: List[str]) -> dict:
"""
Create preview analysis result for a video ID.
Args:
video_id: YouTube or content identifier
warnings: List of warning messages from processing
Returns:
Complete analysis summary dict
"""
model = PreviewModel()
context = {"warnings": warnings} if warnings else None
result = model.analyze(video_id, context)
return result
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