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| import cv2 | |
| import numpy as np | |
| import torch | |
| import torchvision.transforms as transforms | |
| from PIL import Image | |
| class DeepfakeDetector: | |
| def __init__(self, model_path=None): | |
| """ | |
| Initialize the deepfake detector with Nvidia AI model | |
| Args: | |
| model_path: Path to the pre-trained Nvidia AI model | |
| """ | |
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| self.model = self._load_model(model_path) | |
| self.transform = transforms.Compose([ | |
| transforms.Resize((256, 256)), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |
| def _load_model(self, model_path): | |
| """ | |
| Load the Nvidia AI model for deepfake detection | |
| Args: | |
| model_path: Path to the pre-trained model | |
| Returns: | |
| Loaded model | |
| """ | |
| # This is a placeholder for the actual model loading code | |
| # In a real implementation, you would load the specific Nvidia AI model here | |
| if model_path: | |
| try: | |
| # Example placeholder for model loading | |
| # model = torch.load(model_path, map_location=self.device) | |
| print(f"Model loaded from {model_path}") | |
| return None # Replace with actual model | |
| except Exception as e: | |
| print(f"Error loading model: {e}") | |
| return None | |
| else: | |
| print("No model path provided, using default detection methods") | |
| return None | |
| def calculate_smi(self, image1, image2): | |
| """ | |
| Calculate Structural Matching Index between two images | |
| Args: | |
| image1: First image (numpy array or path) | |
| image2: Second image (numpy array or path) | |
| Returns: | |
| SMI score (float between 0 and 1) | |
| """ | |
| # Convert paths to images if needed | |
| if isinstance(image1, str): | |
| image1 = cv2.imread(image1) | |
| image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB) | |
| if isinstance(image2, str): | |
| image2 = cv2.imread(image2) | |
| image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB) | |
| # Ensure images are the same size | |
| if image1.shape != image2.shape: | |
| image2 = cv2.resize(image2, (image1.shape[1], image1.shape[0])) | |
| # Calculate SMI (similar to SSIM but adapted for deepfake detection) | |
| # This is a simplified version - in a real implementation, you would use | |
| # the specific SMI calculation from the Nvidia AI model | |
| gray1 = cv2.cvtColor(image1, cv2.COLOR_RGB2GRAY) | |
| gray2 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY) | |
| # Using SSIM as a placeholder for SMI | |
| from skimage.metrics import structural_similarity as ssim | |
| smi_score, _ = ssim(gray1, gray2, full=True) | |
| return smi_score | |
| def generate_difference_image(self, image1, image2): | |
| """ | |
| Generate a difference image highlighting areas of discrepancy | |
| Args: | |
| image1: First image (numpy array or path) | |
| image2: Second image (numpy array or path) | |
| Returns: | |
| Difference image (numpy array) | |
| """ | |
| # Convert paths to images if needed | |
| if isinstance(image1, str): | |
| image1 = cv2.imread(image1) | |
| image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB) | |
| if isinstance(image2, str): | |
| image2 = cv2.imread(image2) | |
| image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB) | |
| # Ensure images are the same size | |
| if image1.shape != image2.shape: | |
| image2 = cv2.resize(image2, (image1.shape[1], image1.shape[0])) | |
| # Convert to grayscale | |
| gray1 = cv2.cvtColor(image1, cv2.COLOR_RGB2GRAY) | |
| gray2 = cv2.cvtColor(image2, cv2.COLOR_RGB2GRAY) | |
| # Compute the absolute difference | |
| diff = cv2.absdiff(gray1, gray2) | |
| # Normalize for better visualization | |
| diff_normalized = cv2.normalize(diff, None, 0, 255, cv2.NORM_MINMAX) | |
| return diff_normalized | |
| def apply_threshold(self, diff_image, threshold=30): | |
| """ | |
| Apply threshold to difference image to highlight significant differences | |
| Args: | |
| diff_image: Difference image (numpy array) | |
| threshold: Threshold value (0-255) | |
| Returns: | |
| Thresholded image (numpy array) | |
| """ | |
| _, thresh = cv2.threshold(diff_image, threshold, 255, cv2.THRESH_BINARY) | |
| return thresh | |
| def detect_bounding_boxes(self, thresh_image, min_area=100): | |
| """ | |
| Detect bounding boxes around areas of significant difference | |
| Args: | |
| thresh_image: Thresholded image (numpy array) | |
| min_area: Minimum contour area to consider | |
| Returns: | |
| List of bounding boxes (x, y, w, h) | |
| """ | |
| # Find contours in the thresholded image | |
| contours, _ = cv2.findContours(thresh_image.astype(np.uint8), | |
| cv2.RETR_EXTERNAL, | |
| cv2.CHAIN_APPROX_SIMPLE) | |
| # Filter contours by area and get bounding boxes | |
| bounding_boxes = [] | |
| for contour in contours: | |
| area = cv2.contourArea(contour) | |
| if area >= min_area: | |
| x, y, w, h = cv2.boundingRect(contour) | |
| bounding_boxes.append((x, y, w, h)) | |
| return bounding_boxes | |
| def draw_bounding_boxes(self, image, bounding_boxes, color=(0, 255, 0), thickness=2): | |
| """ | |
| Draw bounding boxes on an image | |
| Args: | |
| image: Image to draw on (numpy array) | |
| bounding_boxes: List of bounding boxes (x, y, w, h) | |
| color: Box color (B, G, R) | |
| thickness: Line thickness | |
| Returns: | |
| Image with bounding boxes | |
| """ | |
| # Make a copy of the image to avoid modifying the original | |
| result = image.copy() | |
| # Draw each bounding box | |
| for (x, y, w, h) in bounding_boxes: | |
| cv2.rectangle(result, (x, y), (x + w, y + h), color, thickness) | |
| return result | |
| def process_image_pair(self, image1, image2, threshold=30, min_area=100): | |
| """ | |
| Process a pair of images through the complete verification pipeline | |
| Args: | |
| image1: First image (numpy array or path) | |
| image2: Second image (numpy array or path) | |
| threshold: Threshold value for difference detection | |
| min_area: Minimum area for bounding box detection | |
| Returns: | |
| Dictionary containing: | |
| - smi_score: Structural Matching Index | |
| - difference_image: Difference visualization | |
| - threshold_image: Thresholded difference image | |
| - bounding_boxes: List of detected bounding boxes | |
| - annotated_image: Original image with bounding boxes | |
| """ | |
| # Load images if paths are provided | |
| if isinstance(image1, str): | |
| img1 = cv2.imread(image1) | |
| img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB) | |
| else: | |
| img1 = image1.copy() | |
| if isinstance(image2, str): | |
| img2 = cv2.imread(image2) | |
| img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB) | |
| else: | |
| img2 = image2.copy() | |
| # Calculate SMI | |
| smi_score = self.calculate_smi(img1, img2) | |
| # Generate difference image | |
| diff_image = self.generate_difference_image(img1, img2) | |
| # Apply threshold | |
| thresh_image = self.apply_threshold(diff_image, threshold) | |
| # Detect bounding boxes | |
| bounding_boxes = self.detect_bounding_boxes(thresh_image, min_area) | |
| # Draw bounding boxes on original image | |
| annotated_image = self.draw_bounding_boxes(img1, bounding_boxes) | |
| # Return results | |
| return { | |
| 'smi_score': smi_score, | |
| 'difference_image': diff_image, | |
| 'threshold_image': thresh_image, | |
| 'bounding_boxes': bounding_boxes, | |
| 'annotated_image': annotated_image | |
| } |