A Programming Lab Cheating Surveillance Software That Uses Keystroke/Mouse/Webcam Sequences To Detect Possible Cheating Scenarios In a Lab Exam Setting Using MT-Cascaded Nueral Networks, Reinforcement Learning Agents and Deep Q-Networks
Reminder = Requires External Download of CMake in the System
This Cheating Surveillance System is designed to monitor user activities on a computer to detect potential cheating or unethical behavior. It logs keystrokes, mouse movements, clipboard contents, and captures frames from a webcam to analyze the user's focus and actions. The system includes a Flask backend that processes and serves the logged data, and a frontend that displays the data and allows interaction, such as viewing captured images and analyzing risk levels associated with the logs.
- Keystroke Logging: Captures all keystrokes along with timestamps and the active window titles.
- Mouse Movement Logging: Tracks mouse movements and logs window switches and other significant events.
- Clipboard Monitoring: Records any text that is copied to the clipboard.
- Webcam Surveillance: Captures frames based on specific triggers such as significant eye movement or leaving the workstation.
- Risk Analysis: Analyzes the collected data to assess the risk level of cheating or unethical behavior.
- Dynamic Reporting: Provides an interface to view detailed logs and the risk analysis results.
- Feedback System: Allows users to provide feedback on the system's risk assessment, which is used to train a reinforcement learning model to improve accuracy.
- Python: Core backend development.
- Flask: Server-side web framework used for handling web requests and serving the web application.
- HTML/CSS/JavaScript: Frontend development for displaying data and interacting with the backend.
- Bootstrap: For responsive design and styled components.
- Reinforcement Learning: Used to enhance risk analysis based on user feedback.
app.py: Main Flask application file with route definitions.ScoringModel.py: Contains the logic for parsing logs, calculating cheating scores, and managing the reinforcement learning agent.templates/: Contains HTML files for the web interface.static/: Contains CSS, JavaScript, and other static files.Eye-Tracker/: Directory storing captured frames and webcam logs.Keylogger/: Directory containing keystroke logs.mousemovement/: Stores logs related to mouse movements.
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Clone the Repository:
git clone https://github.com/Tahiralira/Cheating-Surveillance.git cd Cheating-Surveillance -
Install Dependencies: To install the required dependencies, run the following command:
pip install -r requirements.txt
This command will install all the necessary packages listed in the requirements.txt file.
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Start the Flask Application:
python app.py -
Access the Web Interface:
- Open a web browser and navigate to
http://127.0.0.1:5000/to view the interface.
- Open a web browser and navigate to
This project uses CMake as the build system. Before building the project, make sure you have CMake installed on your system.
You can download and install CMake from the official website. Alternatively, you can use your system's package manager to install CMake.
sudo apt-get update
sudo apt-get install cmake- View Logs: Click on the respective buttons to load different types of logs (keyboard, mouse, webcam).
- Analyze Risk: Click on the 'Analyze Logs' button to see the risk analysis based on the collected data.
- Submit Feedback: Use the feedback form to submit your assessment of the risk level, which will help train the system.
Contributions to the project are welcome. Please follow the standard pull request process to submit enhancements or fixes.
This project is licensed under me.
Aheed Tahir