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 }