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import numpy as np
import cv2
class ThreatLabeler:
def __init__(self):
"""
Initialize the threat labeling system for deepfake detection
"""
# Define threat level thresholds
self.threat_levels = {
'low': (0, 30), # Low threat: 0-30% difference
'medium': (30, 60), # Medium threat: 30-60% difference
'high': (60, 100) # High threat: 60-100% difference
}
# Define colors for each threat level (BGR format for OpenCV)
self.threat_colors = {
'low': (0, 255, 0), # Green
'medium': (0, 165, 255), # Orange
'high': (0, 0, 255) # Red
}
def calculate_threat_level(self, diff_value):
"""
Calculate threat level based on difference value
Args:
diff_value: Normalized difference value (0-100)
Returns:
Threat level string ('low', 'medium', or 'high')
"""
for level, (min_val, max_val) in self.threat_levels.items():
if min_val <= diff_value < max_val:
return level
return 'high' # Default to high if outside ranges
def calculate_region_threat(self, diff_image, bbox):
"""
Calculate the threat level for a specific region
Args:
diff_image: Difference image (normalized 0-255)
bbox: Bounding box (x, y, w, h)
Returns:
Threat level string and average difference value
"""
x, y, w, h = bbox
region = diff_image[y:y+h, x:x+w]
# Calculate average difference in the region (normalized to 0-100)
avg_diff = np.mean(region) / 255 * 100
# Determine threat level
threat_level = self.calculate_threat_level(avg_diff)
return threat_level, avg_diff
def label_regions(self, image, diff_image, bounding_boxes):
"""
Label regions with threat levels and colors
Args:
image: Original image to label
diff_image: Difference image
bounding_boxes: List of bounding boxes (x, y, w, h)
Returns:
Labeled image and list of regions with threat levels
"""
# Make a copy of the image to avoid modifying the original
labeled_image = image.copy()
labeled_regions = []
# Process each bounding box
for bbox in bounding_boxes:
x, y, w, h = bbox
# Calculate threat level for this region
threat_level, avg_diff = self.calculate_region_threat(diff_image, bbox)
# Get color for this threat level
color = self.threat_colors[threat_level]
# Draw colored rectangle based on threat level
cv2.rectangle(labeled_image, (x, y), (x + w, y + h), color, 2)
# Add label text with threat level and percentage
label_text = f"{threat_level.upper()}: {avg_diff:.1f}%"
cv2.putText(labeled_image, label_text, (x, y - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
# Store labeled region info
labeled_regions.append({
'bbox': bbox,
'threat_level': threat_level,
'difference_percentage': avg_diff
})
return labeled_image, labeled_regions
def get_threat_summary(self, labeled_regions):
"""
Generate a summary of threat levels in the image
Args:
labeled_regions: List of labeled regions with threat levels
Returns:
Dictionary with threat summary statistics
"""
if not labeled_regions:
return {
'total_regions': 0,
'threat_counts': {'low': 0, 'medium': 0, 'high': 0},
'max_threat': None,
'average_difference': 0
}
# Count regions by threat level
threat_counts = {'low': 0, 'medium': 0, 'high': 0}
total_diff = 0
max_threat = {'level': 'low', 'percentage': 0}
for region in labeled_regions:
level = region['threat_level']
diff = region['difference_percentage']
# Update counts
threat_counts[level] += 1
total_diff += diff
# Track maximum threat
if diff > max_threat['percentage']:
max_threat = {'level': level, 'percentage': diff}
# Calculate average difference
avg_diff = total_diff / len(labeled_regions) if labeled_regions else 0
return {
'total_regions': len(labeled_regions),
'threat_counts': threat_counts,
'max_threat': max_threat,
'average_difference': avg_diff
} |