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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
# Load the saved model
model_path = "deepfake_detector.h5"
model = load_model(model_path)
# Image dimensions
img_height, img_width = 128, 128
# Trained model prediction
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):
"""Predict whether a video is real or fake by analyzing frames."""
try:
cap = cv2.VideoCapture(video_path)
fake_count, real_count = 0, 0
total_frames = 0
results = {}
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Process every 5th frame to improve performance
if total_frames % 5 == 0:
# Analyze frame using all detection methods
frame_path = f"temp_frame_{total_frames}.jpg"
cv2.imwrite(frame_path, frame)
frame_results = combined_prediction(frame_path)
if frame_results["Final Prediction"] == "Fake":
fake_count += 1
else:
real_count += 1
os.remove(frame_path)
total_frames += 1
cap.release()
# Calculate final results
total_analyzed_frames = fake_count + real_count
fake_percentage = (fake_count / total_analyzed_frames * 100) if total_analyzed_frames > 0 else 0
results["Total Frames Analyzed"] = total_analyzed_frames
results["Fake Frames"] = fake_count
results["Real Frames"] = real_count
results["Fake Percentage"] = round(fake_percentage, 2)
results["Final Video Prediction"] = "Fake" if fake_percentage > 50 else "Real"
results["Confidence Score"] = round(abs(50 - fake_percentage) / 50, 2)
return results
except Exception as e:
return {"Error": f"Error analyzing video: {str(e)}"}
# Metadata analysis
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)}"
# Artifact density analysis
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)}"
# Noise pattern detection
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)}"
# Symmetry analysis
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)}
# Combine all methods
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
# Main function
if __name__ == "__main__":
test_image_path = "C:/Users/ramya/OneDrive - iiit-b/Desktop/test1.jpg"
if os.path.exists(test_image_path):
final_results = combined_prediction(test_image_path)
print("\nCombined Prediction Results:")
for key, value in final_results.items():
if isinstance(value, dict):
print(f"{key}:")
for sub_key, sub_value in value.items():
print(f" {sub_key}: {sub_value}")
else:
print(f"{key}: {value}")
# if __name__ == "__main__":
# # Test video
# test_video_path = "path/to/your/video.mp4"
# if os.path.exists(test_video_path):
# video_results = predict_video(test_video_path)
# print("\nVideo Analysis Results:")
# for key, value in video_results.items():
# print(f"{key}: {value}")
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