| import os |
| import cv2 |
| import numpy as np |
| import imghdr |
| from tensorflow.keras.models import load_model |
| from tensorflow.keras.preprocessing import image |
| from PIL import Image |
| from PIL.ExifTags import TAGS |
|
|
| |
| model_path = "C:/Users/krish/OneDrive - iiit-b/IIITB/Hackathons/Truthtell/complete_nlp_stuff/deepfake2/deepfake_detector.h5" |
| model = load_model(model_path) |
|
|
| |
| img_height, img_width = 128, 128 |
|
|
| |
| def predict_image(img_path): |
| if not os.path.exists(img_path): |
| return "Image path does not exist." |
| img = image.load_img(img_path, target_size=(img_height, img_width)) |
| img_array = image.img_to_array(img) / 255.0 |
| img_array = np.expand_dims(img_array, axis=0) |
| prediction = model.predict(img_array) |
| return "Fake" if prediction[0][0] > 0.5 else "Real" |
|
|
| |
| def predict_video(video_path): |
| if not os.path.exists(video_path): |
| return "Video path does not exist." |
| |
| cap = cv2.VideoCapture(video_path) |
| fake_frames = 0 |
| total_frames = 0 |
| |
| while cap.isOpened(): |
| ret, frame = cap.read() |
| if not ret: |
| break |
| |
| |
| frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) |
| frame_resized = cv2.resize(frame_rgb, (img_height, img_width)) |
| |
| |
| frame_array = image.img_to_array(frame_resized) / 255.0 |
| frame_array = np.expand_dims(frame_array, axis=0) |
| |
| |
| prediction = model.predict(frame_array) |
| if prediction[0][0] > 0.5: |
| fake_frames += 1 |
| total_frames += 1 |
| |
| cap.release() |
| |
| |
| fake_percentage = (fake_frames / total_frames) * 100 if total_frames > 0 else 0 |
| |
| return { |
| "prediction": "Fake" if fake_percentage > 50 else "Real", |
| "confidence_score": round(fake_percentage / 100, 2), |
| "frames_analyzed": total_frames, |
| "fake_frames_detected": fake_frames |
| } |
|
|
| |
| def check_metadata(img_path): |
| try: |
| img = Image.open(img_path) |
| exif_data = img._getexif() |
| if not exif_data: |
| return "Fake (missing metadata)" |
| metadata = {TAGS.get(tag): value for tag, value in exif_data.items() if tag in TAGS} |
| return "Real (metadata present)" if metadata else "Fake (missing metadata)" |
| except Exception as e: |
| return f"Error analyzing metadata: {str(e)}" |
|
|
| |
| def analyze_artifacts(img_path): |
| try: |
| img = cv2.imread(img_path) |
| img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
| laplacian = cv2.Laplacian(img_gray, cv2.CV_64F) |
| mean_var = np.mean(np.var(laplacian)) |
| return "Fake (high artifact density)" if mean_var > 10 else "Real" |
| except Exception as e: |
| return f"Error analyzing artifacts: {str(e)}" |
|
|
| |
| def detect_noise_patterns(img_path): |
| try: |
| img = cv2.imread(img_path) |
| img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
| noise_std = np.std(img_gray) |
| return "Fake (unnatural noise patterns)" if noise_std < 5 else "Real" |
| except Exception as e: |
| return f"Error analyzing noise patterns: {str(e)}" |
|
|
| |
| def calculate_symmetry(img_path): |
| try: |
| img = cv2.imread(img_path) |
| img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) |
| img_flipped_v = cv2.flip(img_gray, 1) |
| img_flipped_h = cv2.flip(img_gray, 0) |
| vertical_symmetry = 1 - np.mean(np.abs(img_gray - img_flipped_v)) / 255 |
| horizontal_symmetry = 1 - np.mean(np.abs(img_gray - img_flipped_h)) / 255 |
| return { |
| "Vertical Symmetry": round(vertical_symmetry, 2), |
| "Horizontal Symmetry": round(horizontal_symmetry, 2) |
| } |
| except Exception as e: |
| return {"Error": str(e)} |
|
|
| |
| def combined_prediction(img_path): |
| results = {} |
| cnn_prediction = predict_image(img_path) |
| results["CNN Prediction"] = cnn_prediction |
| cnn_score = 1 if cnn_prediction == "Fake" else 0 |
| metadata_result = check_metadata(img_path) |
| results["Metadata Analysis"] = metadata_result |
| metadata_score = 1 if "Fake" in metadata_result else 0 |
| artifact_result = analyze_artifacts(img_path) |
| results["Artifact Analysis"] = artifact_result |
| artifact_score = 1 if "Fake" in artifact_result else 0 |
| noise_result = detect_noise_patterns(img_path) |
| results["Noise Pattern Analysis"] = noise_result |
| noise_score = 1 if "Fake" in noise_result else 0 |
| symmetry_results = calculate_symmetry(img_path) |
| results["Symmetry Analysis"] = symmetry_results |
| vertical_symmetry = symmetry_results.get("Vertical Symmetry", 0) |
| horizontal_symmetry = symmetry_results.get("Horizontal Symmetry", 0) |
| symmetry_score = 0 |
| if vertical_symmetry != "Unknown" and horizontal_symmetry != "Unknown": |
| if vertical_symmetry > 0.9 or horizontal_symmetry > 0.9: |
| symmetry_score = 1 |
| total_score = (cnn_score * 0.4 + metadata_score * 0.1 + |
| artifact_score * 0.15 + noise_score * 0.15 + |
| symmetry_score * 0.2) |
| results["Final Prediction"] = "Fake" if total_score > 0.5 else "Real" |
| results["Confidence Score"] = round(total_score, 2) |
| return results |
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