About The Project

Abstract - Academic cheating is the use of prohibited methods to gain an unlawful advantage during academic tests and examinations. This study proposes the use of cutting-edge machine learning, particularly deep learning, to utilize pose estimation on examinees to determine if they are cheating. A proctor’s monitoring system was developed alongside the assistive monitoring device using the Nvidia Jetson Nano. A web application was developed to allow the proctor to observe the video feed captured by the device, control the pose estimation and cheating detection features, and review previously stored evidence. The developed system provides real-time capabilities close to 10 frames per second under the full computational load. Benchmarked on a validated dataset, the system was evaluated with an accuracy of 90%, an f1-score of 89.65%, and an area nder the receiver operating characteristic curve (AUROC) of 0.32%. A demonstrated survey to proctors yields complete greement on the system’s overall effectiveness.

See the complete paper here!

This study has won 1st Runner up in the Ateneo de Davao University Engineering Thesis Awards. See here!

This project is no longer under active development but welcomes changes/improvements

Built With

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Getting Started

To get a local copy up and running follow these simple example steps.

Prerequisites

Requirements for the default configuration (you might need more resources with a greater --net_resolution and/or scale_number or less resources by reducing the net resolution and/or using the MPI and MPI_4 models):
- CUDA (Nvidia GPU) version:
  - NVIDIA graphics card with at least 1.6 GB available (the nvidia-smi command checks the available GPU memory in Ubuntu).
  - At least 2.5 GB of free RAM memory for BODY_25 model or 2 GB for COCO model (assuming cuDNN installed).
  - Highly recommended: cuDNN.
- OpenCL (AMD GPU) version:
  - Vega series graphics card
  - At least 2 GB of free RAM memory.
- CPU-only (no GPU) version:
  - Around 8GB of free RAM memory.
- Highly recommended: a CPU with at least 8 cores.
Dependencies:
- OpenCV (all 2.X and 3.X versions are compatible).

Installation

Clone the repo

git clone https://github.com/gembancud/Cheating-Detection.git

Install Python packages
```
pip install -r requirements.txt
```
- Download Compiled OpenPose Models + (Windows)Pyopenpose and move to:
- Download (Linux)Pyopenpose library without CuDnn and move to:
- Download (Linux)Pyopenpose library with CuDnn and move to:
```
Cheating-Detection/CheatDetection/
```

Usage

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For more examples, please refer to the Documentation

Roadmap

See the open issues for a list of proposed features (and known issues).

Author's personal documentation hosted on Google Sheets

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

Fork the Project
Create your Feature Branch (git checkout -b feature/AmazingFeature)
Commit your Changes (git commit -m 'Add some AmazingFeature')
Push to the Branch (git push origin feature/AmazingFeature)
Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Gil Emmanuel Bancud - @iamuPnP - gembancud@gmail.com

Project Link: https://github.com/gembancud/Cheating-Detection

gembancud / Cheating-Detection

readme

Live Exam Cheating Detection

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