The document proposes developing an automatic cheating detection system for exam halls using machine learning. It aims to identify various forms of cheating like copying or communicating without invading privacy. The system will use YOLOv3 object detection and ShuffleNets architecture for real-time detection from multiple camera feeds. When cheating is detected, the system will promptly alert administrators for intervention while minimizing false positives and negatives. The system is intended to enhance exam integrity and fairness while reducing human errors.

1. AUTOMATIC CHEATING DETECTION
IN EXAM HALL
Mr.PRAVEEN
ASST PROFESSOR)
ROOPIKHA.S-200701201
ROSHINI.S-200701202
RITHIKA.S-200701198
Department of Computer Science and Engineering
CS19811 – Project Phase-II

2. Problem Statement and Motivation
The Aim is to develop an automatic cheating detection system using machine
learning techniques to identify and prevent cheating behavior during exams
conducted in an exam hall environment this system can detect various forms of
cheating such as copying from materials, communicating with others using electronic
devices or other suspicious behavior while minimizing false positive and negatives
cheating during exams determines the integrity of the education system and
diminishes the value of academic achievement by using automatic cheating detection
system provides more efficient and accurate solution by leveraging advance
technologies like computer vision and data analysis this system ensure fairness and
maintain the exam protocol.
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3. Objectives
Implementing an automatic cheating detection system in exam halls using machine
learning has the following objectives:
1. Enhancing Exam Integrity
2. Minimizing False Positives
3. Ensuring Scalability
4. Reducing Human Errors
5. Achieving Cost and Resource Efficiency
These objectives aim to provide a comprehensive and reliable solution to maintain the
credibility of exams and uphold the principles of academic honesty.
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4. Abstract
An innovative cheating detection system for exam halls using machine learning is
being introduced. By utilizing computer vision and real-time monitoring, the system
can identify various cheating behaviors, thus ensuring exam integrity. This model's
training minimizes errors and false alerts, optimizing accuracy.
Automated alerts help administrators take prompt interventions to uphold fairness and
credibility. The system also adheres to privacy regulations, safeguarding student data.
This innovative system enhances cheating prevention and maintains the essence of
academic honesty.
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5. System Architecture
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6. System Architecture
Existing System
The existing methods for cheating detection in exam halls often rely on manual
monitoring by invigilators, which can be time-consuming and prone to human
errors.these methods include: visual inspection, surveillance cameras ,proctoring
software .these, methods have limitations such as limited coverage, potential privacy
concerns and inabilities to detect subtle cheating methods.
Proposed System
The proposed system for automatic cheating detection in exam halls will use
YOLOv3 with ShuffleNets to integrate cutting-edge technologies that enhance
accuracy, efficiency, and ethical considerations in monitoring exam environments.
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7. System Architecture
The proposed system is made up of several components that work together to
maintain the integrity of an exam hall. These components include the integration of
the YOLOv3 object detection algorithm, which identifies cheating behaviors or
objects such as smartphones, notes, or collaboration between students with high
accuracy. Additionally, the system uses the ShuffleNets architecture to optimize
computational efficiency, enabling real-time detection while reducing computational
resources. A network of strategically-placed cameras provides comprehensive
coverage for video input to the system. To accurately distinguish between normal
activities and potential cheating incidents, the system is trained to classify specific
cheating behaviors. It continuously monitors the exam hall through live video
analysis and immediately flags any suspicious activity through an alert mechanism
developed to notify proctors or administrators in real-time.
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8. System architecture
System Operation:
The system is designed to adapt to varying lighting conditions, camera angles, or
exam hall layouts for consistent and reliable performance. The system operates
ethically by respecting individual privacy and focusing solely on relevant cheating
behaviors without unnecessary intrusion. A user-friendly interface is developed for
proctors or administrators, allowing them to monitor the system, review flagged
instances, and take appropriate actions effectively. In operation, the proposed system
continuously analyzes live video feeds from multiple cameras using YOLOv3 with
ShuffleNets, enabling real-time detection of cheating behaviors. Upon identifying
suspicious activities, the system promptly alerts administrators via the user interface,
where they can review flagged instances and take appropriate actions to maintain the
integrity of the examination process.
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9. List of Modules
1. Clearly define the objectives and goals of your project:
What exactly do you want to achieve with automatic cheating detection using
YOLOv5
2. Identify the Stakeholders:
Identify all the key stakeholders, including end-users, project sponsors, and anyone
else who will be impacted by or involved in the project.
3. Gather Requirements:
Collect and document the specific requirements for the cheating detection system.
These may include data sources, performance metrics, and user interface
requirements.
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10. List Of Modules
4. Data Collection and Annotation:
Determine what data you will need for training and testing your YOLOv5 model.
This includes images or videos of test-taking environments. Annotate the data to
identify cheating activities.
5. Model Selection:
Decide on the use of YOLOv5 as the object detection framework. Assess its
suitability for your specific project requirements.
6. Hardware and Software Requirements:
Identify the hardware and software infrastructure needed to run YOLOv5 efficiently.
Consider GPU/CPU resources, storage, and software dependencies.
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11. List Of Modules
7. Team Formation:
Assemble a team with the required skills, such as machine learning engineers, data
scientists, and software developers.
8. Project Timeline:
Create a detailed project timeline that outlines milestones, deadlines, and
deliverables.
10. Data Preprocessing:
Clean, preprocess, and augment the data as necessary for training and testing the
YOLOv5 model. Ensure data privacy and security considerations are met.
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12. Functional Description for each modules with
DFD and Activity Diagram
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13. Implementation/Results of Module
Project Description for the First Module:
Objective:
The primary objective of the first module is to develop an automatic cheating
detection system using YOLOv3 object detection with ShuffleNets architecture,
specifically designed for monitoring and preventing cheating behaviors in an exam
hall or similar testing environments.
Key Features of Functionalities:
1. Real-time Object Detection: Our system uses YOLOv3 (You Only Look Once) to
provide accurate object detection in real-time.
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14. Implementation/Results of Module
2. Integration of ShuffleNets: Our system utilizes ShuffleNets architecture to
optimize and streamline the detection process. This enables efficient use of
computational resources while maintaining accuracy.
3. Detection of Cheating Behaviors: We have developed algorithms to detect cheating
behaviors such as looking at unauthorized materials, communication between
candidates, or suspicious actions.
4. Integration with Exam Monitoring System: Our cheating detection system is
integrated with an exam monitoring setup to trigger alerts or notifications when
suspicious activities are detected.
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15. Implementation/Results of Module
5. Data Logging and Reporting: Our system captures and logs instances of suspicious
behavior for further review and analysis by authorized personnel.
This module is designed to cater to the needs of educational institutions, examination
centers, or any organization that conducts exams where maintaining the integrity of
the testing process is of utmost importance. The primary goal of this module is to
assist invigilators or administrators in managing and monitoring exam halls to ensure
that examinations are conducted fairly and by the established guidelines.
Technologies and Tools:
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16. Implementation/Results of Module
We have implemented the YOLOv3 deep learning model to detect objects in real
time. To optimize computational efficiency without compromising accuracy, we have
employed the ShuffleNets architecture. Python is the primary programming language
we use for implementation. Additionally, we have leveraged popular deep-learning
libraries such as TensorFlow and PyTorch for model training and inference.
The estimated timeline for the completion of the first module is as follows: Research
and Design: 1 month, Model Development and Integration: 2 months, Testing and
Refinement: 1.5 months, Integration with Exam Monitoring System: 1 month.
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17. Implementation/Results of Module
The initial module's outcomes aim for robust cheating detection in exam halls using
YOLOv3 with ShuffleNets. Expected results include high accuracy in identifying
cheating behaviors (potentially 90%), efficient real-time performance (averaging 0.1
seconds per frame), and minimized false positives (around 5%) and false negatives
(about 8%). Successful integration with exam monitoring systems, triggering alerts in
95% of detected cheating instances, is crucial. Preliminary performance metrics such
as precision (91%), recall (88%), and F1-score (89%) will be indicators. These
outcomes serve as the foundation for subsequent improvements, guiding further
iterations. Any shortcomings will prompt retraining with more diverse datasets,
adjustments in model parameters, or architectural modifications to optimize the
system's performance. User feedback and real-world testing will drive refinements in
subsequent modules for a more reliable and comprehensive cheating detection system
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18. Pending Modules & Conclusion
The proposed model was implemented using Python with Tensor Flow on a core i7
system with Nvidia 1080 ti, and trained with a learning rate of 0.0002, batch size of
1, for 100 epochs, and momenta of 0.091. The training started with a loss of 4.02323,
which gradually reduced to 0.0023. The model was trained on 75% of the total
dataset, with 15% used for validation and 10% for testing. The proposed model
achieved an accuracy of 88.03% on our dataset, while the YOLOv3 model with
transfer learning scored 82.06% accuracy. The proposed model performed better than
the typical YOLOv3, both in terms of time and accuracy. Below Table shows the
detection results of the prepared dataset trained on both deep learning models. The
average precision (AP) of both models indicates that the proposed model
outperformed YOLOv3 with 88.03% accuracy in detecting cheating.
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19. References
[1]H. Cai and I. King, “Education Technology for Online Learning in Times of
Crisis,” in Proceedings of 2020 IEEE International Conference on Teaching,
Assessment, and Learning for Engineering, TALE 2020, Institute of Electrical and
Electronics Engineers Inc., Dec. 2020, pp. 758–763. doi:
10.1109/TALE48869.2020.9368387.
[2]L. Al-Labadi and S. Sant, “Enhance Learning Experience Using Technology In
Class,” J Technol Sci Educ, vol. 11, no. 1, pp. 44–52, Sep. 2021, doi:
10.3926/jotse.1050.
[3]M. Alahmari, “Exploring the Influential Factors Affecting Staff Willingness to
Adopt Augmented Reality,” International Journal of Information and Edu- cation
Technology, vol. 13, no. 7, pp. 1078–1084, Jul. 2023
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20. References
[4]S. R. Sletten, “Rethinking Assessment: Replacing Traditional Exams with Paper
Reviews,” J Microbiol Biol Educ, vol. 22, no. 2, Sep. 2021, doi:
10.1128/jmbe.00109-21.
[5]É. Cambron-Goulet, J. P. Dumas, É. Bergeron, L. Bergeron, and C. St-Onge,
“Guidelines for Creating Written Clinical Reasoning Exams: Insight from a Delphi
Study,” Health Professions Education, vol. 5, no. 3, pp. 237–247, Sep. 2019, doi:
10.1016/j.hpe.2018.09.001.
[6]R. Ellis, J. Cleland, D. SG. Scrimgeour, A. J. Lee, J. Hines, and P. A. Bren- nan,
“Establishing the predictive validity of the intercollegiate membership of the Royal
Colleges of surgeons written examination: MRCS Part A,” The Surgeon, Aug. 2023,
doi: 10.1016/j.surge.2023.07.004.
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21. References
[7]M. J. Hoque, Md. Razu, Md. Jashim, and M. Mostafa, “Automation of Tradi-
tional Exam Invigilation using CCTV and Bio-Metric,” International Journal of
Advanced Computer Science and Applications, vol. 11, no. 6, pp. 392–399, 2020,
doi: 10.14569/IJACSA.2020.0110651.
[8]R. E. Ferdig, E. Baumgartner, R. Hartshorne, R. Kaplan-Rakowski, and C. Mouza,
“Teaching, Technology, and Teacher Education During the COVID- 19 Pandemic:
Stories From The Field,” AACE-Association for the Advance- ment of Computing in
Education, pp. 125–128, Jun. 2020.
[9]R. Comas-Forgas, T. Lancaster, A. Calvo-Sastre, and J. Sureda-Negre, “Exam
cheating and academic integrity breaches during the COVID-19 pandemic: An
analysis of internet search activity in Spain,” Heliyon, vol. 7, no. 10, Oct. 2021.
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22. Paper Publication Status (Phase-I & Phase-II)
PHASE-I
SUSMITA MISHRA, ROOPIKHA S,ROSHINI S,RITIKHA S (2023, November) AUTOMATIC CHEATING DETECTION IN EXAM
HALL.Scopus-Conference.
TITLE:AUTOMATIC CHEATING DETECTION IN EXAM HALL
AUTHOR:SUSMITA MISHRA , ROOPIKHA S, ROSHINI S,
RITIKHA S
CONFERENCE NAME: International Journal of Innovative Science and Modern Engineering (IJISME)
CONFERENCE:Scopus Indexed - Conference
MODEL OF PUBLICATION: OFFLINE
STATUS:ACCEPTED
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23. Paper Publication Status (PhaseI & PhaseII)
PHASE-II
ROOPIKHA S,ROSHINI S,RITIKHA S
AUTOMATIC CHEATING DETECTION IN EXAM HALL.
TITLE:AUTOMATIC CHEATING DETECTION IN EXAM HALL
MODEL OF PUBLICATION:OFFLINE
For phaseII the publication of journal is ongoing yet not completed
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24. Thank You
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