| """ |
| Example: Using the model for deepfake detection |
| """ |
|
|
| import torch |
| from torchvision import transforms |
| from PIL import Image |
| from model import load_model |
| import json |
|
|
| |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
| model = load_model('model_best_checkpoint.ckpt', device=device) |
|
|
| |
| 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}") |
|
|
| |
| 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) |
| """ |
| |
| image = Image.open(image_path).convert('RGB') |
| input_tensor = transform(image).unsqueeze(0).to(device) |
| |
| |
| with torch.no_grad(): |
| error = model.reconstruction_error(input_tensor, reduction='none') |
| |
| error_value = error.item() |
| is_fake = error_value > threshold |
| |
| |
| confidence = min(abs(error_value - threshold) / threshold, 1.0) |
| |
| return is_fake, error_value, confidence |
|
|
| |
| 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%}") |
|
|
| |
| 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 |
|
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