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# -*- coding: utf-8 -*-
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
import io
import base64
class EnsembleImageDetector:
def __init__(self):
"""Load multiple models for better accuracy"""
print("Loading ensemble image detectors...")
self.models = []
model_names = [
"umm-maybe/AI-image-detector",
"Organika/sdxl-detector"
]
for model_name in model_names:
try:
print(f" Loading {model_name}...")
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
model.eval()
self.models.append({
'name': model_name,
'processor': processor,
'model': model
})
print(f" ✓ {model_name} loaded")
except Exception as e:
print(f" ✗ Failed to load {model_name}: {e}")
if len(self.models) == 0:
raise Exception("Failed to load any models!")
print(f"Loaded {len(self.models)} models for ensemble\n")
def detect_from_base64(self, base64_string):
"""Detect using ensemble voting"""
try:
if ',' in base64_string:
base64_string = base64_string.split(',')[1]
image_data = base64.b64decode(base64_string)
image = Image.open(io.BytesIO(image_data)).convert('RGB')
return self.detect_from_image(image)
except Exception as e:
print(f"Error decoding image: {e}")
raise
def detect_from_image(self, image):
"""Ensemble detection with voting and metadata analysis"""
width, height = image.size
total_pixels = width * height
megapixels = total_pixels / 1000000
print(f"Analyzing: {width}x{height} ({megapixels:.1f}MP)")
# Get predictions from all models
predictions = []
for model_info in self.models:
try:
inputs = model_info['processor'](images=image, return_tensors="pt")
with torch.no_grad():
outputs = model_info['model'](**inputs)
probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
if probs.shape[1] == 2:
ai_prob = probs[0][1].item()
else:
ai_prob = probs[0][0].item()
predictions.append(ai_prob)
print(f" Model prediction: {ai_prob*100:.1f}% AI")
except Exception as e:
print(f" Model error: {e}")
if not predictions:
raise Exception("All models failed!")
# Average predictions
avg_ai_prob = sum(predictions) / len(predictions)
# Metadata analysis
has_exif = False
exif_count = 0
try:
exif = image.getexif()
if exif:
exif_count = len(exif)
has_exif = exif_count > 8
except:
pass
# Check AI characteristics
aspect_ratio = width / height
is_square = 0.95 < aspect_ratio < 1.05
common_ai_sizes = [512, 768, 1024, 1536, 2048]
is_ai_size = width in common_ai_sizes and height in common_ai_sizes
# Strong indicators
strong_real = sum([has_exif, megapixels > 8, not is_ai_size])
strong_ai = sum([exif_count == 0, is_square, is_ai_size])
# Apply calibration
final_prob = avg_ai_prob
if strong_real >= 2:
final_prob = final_prob * 0.5
elif has_exif:
final_prob = final_prob * 0.6
if strong_ai >= 2:
final_prob = final_prob * 1.3
final_prob = final_prob * 0.9
final_prob = max(0.05, min(0.95, final_prob))
print(f"Final: {final_prob*100:.1f}% AI")
# Generate explanations
explanations = self._generate_explanations(
has_exif, is_square, is_ai_size, megapixels, width, height, final_prob
)
distance = abs(final_prob - 0.5)
confidence = "High" if distance > 0.3 else "Medium" if distance > 0.2 else "Low"
return {
'prediction': 'AI' if final_prob > 0.5 else 'Real',
'ai_probability': round(final_prob * 100, 2),
'real_probability': round((1 - final_prob) * 100, 2),
'confidence': confidence,
'explanations': explanations
}
def _generate_explanations(self, has_exif, is_square, is_ai_size, mp, w, h, prob):
"""Generate user-friendly explanations"""
explanations = []
if has_exif:
explanations.append({
'indicator': 'Camera Metadata Detected',
'description': 'Image contains extensive EXIF data with camera settings, strongly suggesting authentic photograph.',
'type': 'Real'
})
else:
explanations.append({
'indicator': 'No Camera Metadata',
'description': 'Missing EXIF data normally present in photos from cameras and smartphones.',
'type': 'AI'
})
if is_ai_size:
explanations.append({
'indicator': 'AI-Standard Dimensions',
'description': f'Image size ({w}x{h}) matches common AI generation formats.',
'type': 'AI'
})
else:
explanations.append({
'indicator': 'Unique Dimensions',
'description': f'Non-standard dimensions ({w}x{h}) typical of real camera sensors.',
'type': 'Real'
})
if mp > 8:
explanations.append({
'indicator': 'High Camera Resolution',
'description': f'Very high resolution ({mp:.1f}MP) typical of modern cameras.',
'type': 'Real'
})
elif mp < 2:
explanations.append({
'indicator': 'Low Resolution',
'description': f'Low resolution ({mp:.1f}MP) common in AI-generated images.',
'type': 'AI'
})
if prob > 0.7:
explanations.append({
'indicator': 'Strong AI Patterns',
'description': 'Multiple models detected characteristic AI generation patterns.',
'type': 'AI'
})
elif prob < 0.3:
explanations.append({
'indicator': 'Authentic Photography',
'description': 'Multiple models confirmed natural photographic characteristics.',
'type': 'Real'
})
else:
explanations.append({
'indicator': 'Uncertain',
'description': 'Modern AI generation is extremely realistic. Consider other evidence.',
'type': 'Neutral'
})
return explanations[:5]
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