inference_example.py

"""
Example: Using the model for deepfake detection
"""

import torch
from torchvision import transforms
from PIL import Image
from model import load_model
import json

# Load model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = load_model('model_best_checkpoint.ckpt', device=device)

# Load calibrated thresholds
with open('thresholds_calibrated.json', 'r') as f:
    config = json.load(f)
    threshold = config['reconstruction_thresholds']['thresholds']['balanced']['value']

print(f"Using threshold: {threshold:.6f}")

# Prepare image preprocessing
transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

def detect_deepfake(image_path, model, threshold, device):
    """
    Detect if an image is likely a deepfake based on reconstruction error.
    
    Args:
        image_path: Path to image file
        model: Loaded autoencoder model
        threshold: MSE threshold for detection
        device: torch device
    
    Returns:
        is_fake: Boolean indicating if image is likely fake
        error: Reconstruction error value
        confidence: Confidence score (0-1)
    """
    # Load and preprocess image
    image = Image.open(image_path).convert('RGB')
    input_tensor = transform(image).unsqueeze(0).to(device)
    
    # Calculate reconstruction error
    with torch.no_grad():
        error = model.reconstruction_error(input_tensor, reduction='none')
    
    error_value = error.item()
    is_fake = error_value > threshold
    
    # Calculate confidence (normalized error relative to threshold)
    confidence = min(abs(error_value - threshold) / threshold, 1.0)
    
    return is_fake, error_value, confidence

# Example usage
image_path = "test_image.jpg"
is_fake, error, confidence = detect_deepfake(image_path, model, threshold, device)

print(f"\nResults for: {image_path}")
print(f"Reconstruction Error: {error:.6f}")
print(f"Threshold: {threshold:.6f}")
print(f"Classification: {'FAKE' if is_fake else 'REAL'}")
print(f"Confidence: {confidence:.2%}")

# Batch processing example
def batch_detect(image_paths, model, threshold, device):
    """Process multiple images at once"""
    images = []
    for path in image_paths:
        img = Image.open(path).convert('RGB')
        images.append(transform(img))
    
    batch = torch.stack(images).to(device)
    
    with torch.no_grad():
        errors = model.reconstruction_error(batch, reduction='none')
    
    results = []
    for i, error in enumerate(errors):
        is_fake = error.item() > threshold
        results.append({
            'path': image_paths[i],
            'error': error.item(),
            'is_fake': is_fake
        })
    
    return results

# Example batch processing
# image_paths = ["img1.jpg", "img2.jpg", "img3.jpg"]
# results = batch_detect(image_paths, model, threshold, device)
# for r in results:
#     print(f"{r['path']}: {'FAKE' if r['is_fake'] else 'REAL'} (error: {r['error']:.6f})")