Keycube performance study against Shoulder Surfing Attacks (SSA)

• File -> scripts/proj3d

This script provides tools for projecting 3D points onto a 2D screen using either orthographic or perspective projection. It also includes visualisation of a 3D cube in both 3D and 2D views, and calculations of the percentage of visible surface area and object coverage in the viewport.

More information can be found using the following link.

• File -> scripts/cubeCalc

This project provides a comprehensive analysis of the visibility and angles of the faces of a cube from various points of view. It includes generating view points on a sphere, calculating visibility, and visualising the results.

• Introduction
• Requirements
• Installation
• Usage
• Functions
• Examples
• Results

This script calculates the visibility and angles of the faces of a cube from a given point of view and visualises the results. It generates view points on a sphere around the cube and evaluates which faces are visible from each view point.

• Python 3.x
• NumPy
• Pandas
• Matplotlib

To install the required packages, run:

pip install numpy pandas matplotlib

To run the script, simply execute:

python scripts/cubeCalc.py

Calculates the angles between the point of view and the faces of a cuboid.

Parameters:

• cuboid_center: numpy.ndarray
  • The center of the cuboid.
• cuboid_dims: numpy.ndarray
  • The dimensions of the cuboid.
• pov: numpy.ndarray
  • The point of view.

Returns:

• list: numpy.ndarray
  • A list of booleans indicating whether each face is visible.
• list: numpy.ndarray
  • A list of the visible surface areas of each face.
• list: numpy.ndarray
  • A list of the outside angles of each face.
• int: int
  • The number of visible faces.

Generates view points on a sphere.

Parameters:

• radius : float
  • The radius of the sphere.
• step : float
  • The step size in degrees.

Returns:

• np.ndarray: The polar coordinates of the view points.
• np.ndarray: The Cartesian coordinates of the view points.

Tests the angles and visibility of the faces of a cube from different points of view.

Parameters:

• cuboid_center: numpy.ndarray
  • The center of the cuboid.
• cuboid_dims: numpy.ndarray
  • The dimensions of the cuboid.
• radius: float
  • The radius of the sphere.
• step: int
  • The step size in degrees.

Returns:

• pd.DataFrame: A DataFrame containing the visibility index for each view point.

Visualises the view points with the maximum visibility index.

Parameters:

• df : pd.DataFrame
  • The DataFrame containing the visibility index for each view point.
• output_file : str
  • The output file for the visualisation.

To run the main analysis and generate the results:

  cuboid_center = np.array([0, 0, 0])
  cuboid_dims = np.array([2, 2, 2])
  radius = 10
  step = 5

The results include a CSV file output.csv containing the visibility and angle calculations, and a visualisation saved as output.png. The visualisation shows the view points with maximum visibility index on a polar and 3D plot.