Skip to content

Pipeline Examples

Deepfake Detection Pipeline

from mukh.pipelines.deepfake_detection import PipelineDeepfakeDetection

# Define model configurations with weights
model_configs = {"resnet_inception": 0.5, "efficientnet": 0.5}

# Create ensemble detector
detector = PipelineDeepfakeDetection(model_configs)

# Run detection
result = detector.detect(
    media_path="assets/images/img1.jpg",
    output_folder="output/deepfake_detection_pipeline",
    save_csv=True,
    num_frames=11
)

print(f"Final Result: {'DEEPFAKE' if result else 'REAL'}")

Model Information

# Print detector information
info = detector.get_model_info()
for key, value in info.items():
    print(f"  {key}: {value}")

Available Models

  • resnet_inception
  • efficientnet

Parameters

  • model_configs: Dictionary of model names and their weights
  • media_path: Path to image or video file
  • output_folder: Output directory for results
  • save_csv: Save detection results to CSV
  • num_frames: Number of frames to analyze for videos