Spaces:
Sleeping
Sleeping
File size: 2,632 Bytes
07fe054 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | """
Utility functions for agentic forensic controller.
"""
from typing import Dict
def convert_to_evidence_dict(features: Dict,
prediction_score: float,
prediction_label: int,
thresholds: Dict) -> Dict:
"""
Convert raw features and prediction to evidence dict format.
Args:
features: Raw feature dictionary
prediction_score: Prediction score (0-1)
prediction_label: 0 for REAL, 1 for FAKE
thresholds: Threshold dictionary for each feature
Returns:
Evidence dict with forensic_cues and feature_contributions
"""
pred_label = 'FAKE' if prediction_label == 1 else 'REAL'
# Build forensic cues
forensic_cues = {}
# Noiseprint mismatch
if 'noiseprint_mismatch' in features:
mismatch = features['noiseprint_mismatch']
threshold = thresholds.get('noiseprint_mismatch', 2.5)
forensic_cues['noiseprint_mismatch'] = {
'value': mismatch,
'threshold': threshold,
'supports': 'fake' if mismatch > threshold else 'real'
}
# Residual energy
if 'residual_energy_p95' in features:
energy = features['residual_energy_p95']
threshold = thresholds.get('residual_energy_p95', 0.08)
forensic_cues['residual_energy_p95'] = {
'value': energy,
'threshold': threshold,
'supports': 'fake' if energy > threshold else 'real'
}
# FFT peakiness
if 'fft_peakiness' in features:
peakiness = features['fft_peakiness']
threshold = thresholds.get('fft_peakiness', 3.0)
forensic_cues['fft_peakiness'] = {
'value': peakiness,
'threshold': threshold,
'supports': 'fake' if peakiness > threshold else 'real'
}
# Feature contributions (simplified - could be more sophisticated)
feature_contributions = {}
for feat_name, feat_value in features.items():
if feat_name in thresholds:
threshold = thresholds[feat_name]
# Contribution is how far from threshold (normalized)
contribution = abs(feat_value - threshold) / max(threshold, 1.0)
feature_contributions[feat_name] = contribution
return {
'forensic_cues': forensic_cues,
'feature_contributions': feature_contributions,
'prediction': {
'label': pred_label,
'score': prediction_score
}
}
|