Adama Science & Technology University School of Electrical Engineering and Computing MSc in Computer Science and Engineering (AI) Second year Seminar in AI. (CSEg 7201) Application of Explainable AI in Detecting Forgery Educational Certificate using Deep Learning Nesrudin Abdulwehab Aliye Submitted to: - Teklu Urgessa (PhD, Associate Professor) January, 2024 Acknowledgement First and foremost, I would like to thank our almighty God for giving me the strength, knowledge, ability and opportunity to undertake this research study and to persevere and complete it satisfactorily. Without his blessings, this achievement would not have been possible. Next, my deepest gratitude goes to my advisor Teklu Urgessa (PHD) for his continuous followup, guidance, suggestions and his constructive comments were very essential in the successful completion of my duty. I would like also to thank my family specially my dad Haj-Abdulwehab Aliye Jemal for supporting me in all of my study and for giving me financial and moral support to achieve my work. Last but not least, I would like to thank our collegues/classmate friends Wondossen Argaw and Wondimu Getachew for their contribution directly and indirectly for success of my study. I Table of contents Acknowledgement .......................................................................................................................I Table of contents ........................................................................................................................ II Proposal Summary .................................................................................................................... III CHAPTER ONE .................................................................................................................... - 1 1. Introduction ........................................................................................................................ - 1 1.1. Background of the study ................................................................................................. - 1 1.2. Motivation of the study ................................................................................................... - 3 1.3. Statement of the problem ................................................................................................ - 3 1.4. Research Questions ......................................................................................................... - 4 1.5. Objectives of the study: ...................................................................................................- 4 1.5.1. General Objectives: ......................................................................................................- 4 1.5.2. Specific Objectives: ..................................................................................................... - 4 1.6. Significance and Expected outcome of the study ........................................................... - 5 1.6.1. Significance of the study ..............................................................................................- 5 1.6.2. Expected outcome, Scope and Limitation of the study ................................................- 5 CHAPTER TWO ................................................................................................................... - 7 2. Literature Review ...............................................................................................................- 7 2.1. Related works ..................................................................................................................- 7 2.2. Literature Review on Detecting Forgery Educational Certificate ...................................- 9 2.2.1. Summary of reviewing related works ........................................................................ - 13 2.2.2. Gap Analysis .............................................................................................................. - 16 CHAPTER THREE ..............................................................................................................- 18 3. Methodology .................................................................................................................... - 18 3.1. Research Methods ......................................................................................................... - 18 3.2. Research Design ............................................................................................................- 19 3.3. Materials ........................................................................................................................- 19 3.4. Procedures ..................................................................................................................... - 21 3.5. Evaluation ..................................................................................................................... - 22 3.6. Limitations .................................................................................................................... - 23 3.7. Conceptual framework .................................................................................................. - 24 CHAPTER FOUR ................................................................................................................- 27 4. Work and Budget Plan ..................................................................................................... - 27 4.1. Work plan ......................................................................................................................- 27 4.2. Budget plan ................................................................................................................... - 28 4.3. Future Work .................................................................................................................. - 29 References: ...........................................................................................................................- 31 - II Proposal Summary Forgery of educational certificates is a serious issue that can have far-reaching consequences. To detect and prevent forgery, educational institutes issuing academic certificates or transcripts must meet certain requirements to ensure the authenticity of the certificates. One method of detecting forgery and fabrication in educational certificates and academic transcripts is by using cryptography and QR codes. This involves converting certificate details into a ciphertext, encoding it in a QR code, and printing the same on the certificate or transcript to be issued. The certificate owners, other institutes, employers, and third-party verifiers can verify certificates for forgery and fabrication using the scanning app of the issuing institute. Another method to prevent certificate fraud is by issuing digital certificates with blockchain technology. This ensures that certificates cannot be forged and verifiers can instantly know if they are fake or not. Explainable artificial intelligence (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. Explainable AI is crucial for an organization in building trust and confidence when putting AI models into production. Bias, often based on race, gender, age or location, has been a long-standing risk in training AI models. Further, AI model performance can drift or degrade because production data differs from training data. This makes it crucial for a business to continuously monitor and manage models to promote AI explainability while measuring the business impact of using such algorithms. Explainable AI can help humans understand and explain machine learning (ML) algorithms, deep learning and neural networks. It also promotes end user trust, model auditability and productive use of AI. It mitigates compliance, legal, security and reputational risks of production AI. As AI becomes more advanced, humans are challenged to comprehend and retrace how the algorithm came to a result. There are many related works on explainable implementation of artificial intelligence in detecting forgery educational certificates. A literature review is a comprehensive summary and analysis of the existing research on a topic. The proposed system contain two methods to detect the fake documents. First, the QR-code scanner which scan the QR-code of the document and detect that document is original or fake. Second, the image processing techniques undergoes three stages: training phase, testing phase, classification to detect the fake documents. In this proposed project, the originality of document is discussed and focused on making the detection of forgery document more robust and reliable. By the Neural network and error value analysis algorithm using image processing system to detect the forgery document. Key Words: - Explainable AI, Educational Certificate, machine learning algorithms, deep learning, neural networks, Forgery Detection, Cryptography, QR Code. III CHAPTER ONE 1. Introduction 1.1. Background of the study Explainable AI (XAI) can play a crucial role in detecting forged educational certificates by enhancing transparency, accuracy, and user trust in the verification process. The text “Applications of Explainable AI in Detecting Forgery in Educational Certificates” refers to the use of artificial intelligence (AI) that is understandable and interpretable by humans (Explainable AI) in identifying fraudulent educational certificates. This involves using AI algorithms to analyze educational certificates and detect any anomalies or discrepancies that might indicate forgery. The “explainable” aspect means that the AI provides clear, understandable reasons for its decisions, making it easier for humans to trust and verify the AI’s findings. For instance, one method involves using cryptography and QR codes. The details of the certificate are converted into a ciphertext, encoded in a QR code, and printed on the certificate. Certificate owners, other institutes, employers, and third-party verifiers can then verify the certificates for forgery using the scanning app of the issuing institute. Explainable AI (XAI) is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. XAI is used to describe an AI model, its expected impact and potential biases. It helps characterize model accuracy, fairness, transparency and outcomes in AI-powered decision making. Another approach uses image processing algorithms and feature point matching tools to identify suspected irregular patterns in the certificate. Yet another method uses the Gray Level Co-occurrence Matrix (GLCM) and Singular Value Decomposition (SVD) algorithm to detect forged scans of educational certificates. These applications of explainable AI not only help in maintaining the integrity of the educational system by preventing the use of forged certificates but also ensure transparency in the process. XAI can be used to improve the fairness and transparency of educational assessments. For instance, XAI can help explain how an AI system arrived at a particular grade or score, making it easier for students to understand and trust the system’s findings. Using fake certificates can have serious consequences. If an employer finds out that an employee has submitted a fake certificate, they can take legal action against the -1- employee. The potential consequences could include termination of employment, legal fines, and even imprisonment in some cases. Fake credentials have become a global problem. Falsified academic credentials are a serious, prevalent, and ever-increasing problem. In 2015, the New York Times reported on a billiondollar industry consisting of 3,300 “diploma mills” that sold fake certificates for all levels of degrees worldwide. Buying totally fake academic certificates is only part of the problem. Those who have degrees may falsify their academic transcripts. This is made easier by the availability of sophisticated technology. Education, as Nelson Mandela best described it, is the most potent weapon that can change the world. Thus, the need to provide quality education with appropriate inspection and control systems is crucial. And one of the mechanisms of doing this is tackling the prevalence of fake documents. The government of Ethiopia has spotted more than 200,000 fake degree certificates, the majority of which are offered to government officials. According higher education quality assurance agency it has traced a university college which had been offering up to 15,000 fake master degrees in a year while other had given counterfeit degrees even after it was closed. According to a study conducted by the Government Expenditure Management and Control Office of Ethiopia some regional offices will badly be affected if the government starts invalidation. The higher education quality assurance agency blamed government officials and justice institutions for collaborating with the university colleges which have been issuing fake certificates. The higher education quality assurance agency said it would taking “decisive action to crack down on degree fraud” that “cheats genuine learners”. Academic credential forgery, also known as diploma mill fraud, is the creation or use of false educational documents to gain employment, higher education, or other benefits. This can include counterfeiting diplomas, transcripts, and other official documents from accredited institutions. Fake qualifications pose a reputational risk to universities and employers. It undermines their legitimacy and reputation and robs honest candidates of opportunities for further education or employment. Fortunately, there are steps that universities and employers can take to protect themselves. These include the use of verification systems, reference checking, and competency-based interviews. -2- 1.2. Motivation of the study Having an occupational certification, which proves your compliance with minimal competency criteria, can be a crucial prerequisite for securing employment in regulated industries. These days, Ethiopia is home to a large number of young individuals with diverse skill sets. Since a large number of professionals have requested occupational assessments over the previous sixteen years. Massive volumes of professional data are entered into the database annually. But as an information technology specialist, I noticed that a number of candidates had failed in many domains when looked at the candidate data. For this reason, it's interesting to analyze and assess the problem domain. That claims that because there is sufficient data for better analysis and decision-making, proper attention must be paid to the assessment and certification processes. 1.3. Statement of the problem Educational institutions increasingly rely on digital certificates to verify credentials. However, forgeries of these certificates are a growing concern, posing a threat to academic integrity and employment opportunities. Deep learning models have shown promising results in detecting forged certificates, but their internal workings are often opaque, making it difficult to understand why a particular certificate is flagged as fraudulent. Existing deep learning approaches for certificate forgery detection lack explainability, making it difficult to understand why a model classifies a certificate as genuine or forged. This opacity raises concerns about fairness, accountability and interpretability. This research aims to bridge this gap by applying Explainable AI (XAI) techniques to deep learning models for educational certificate forgery detection. We will develop a system that not only achieves high accuracy but also provides clear explanations for its classifications. To address these challenges and ensure the integrity of workforce certification system, a more efficient, reliable, and transparent approach to detecting forged educational certificates is necessary. This is where Explainable Artificial Intelligence (XAI) and deep learning offer a promising solution. -3- 1.4. Research Questions The questions on the Application of Explainable AI in Forgery Certificate in artificial intelligence (AI) include: 1. How can machine learning algorithms be effectively trained to identify various types of forgery in educational certificates (e.g., altered text, fake stamps, paper inconsistencies)? 2. What features and patterns within certificates are most effective for distinguishing forgeries from genuine documents? 3. How can we ensure the robustness of such systems against potential adversarial attacks from sophisticated forgers? 1.5. Objectives of the study: 1.5.1. General Objectives: This research aims to investigate the application of Explainable Artificial Intelligence (XAI) techniques for detecting forgeries in educational certificates. The focus will be on understanding how XAI can improve the interpretability and trustworthiness of automated forgery detection systems. 1.5.2. Specific Objectives: The specific objectives of detecting forgery educational certificates are to:  Develop accurate and scalable machine learning models that can effectively identify various types of forgery in certificates (e.g., altered text, fake stamps, paper inconsistencies).  Identify key features and patterns within certificates that serve as reliable indicators of forgery.  Ensure the robustness of detection systems against potential adversarial attacks from skilled forgers.  Explore and integrate other relevant technologies like image analysis, forensics techniques, and blockchain technology to enhance detection accuracy and security. -4- 1.6. Significance and Expected outcome of the study 1.6.1. Significance of the study Detecting forgery in educational certificates is a crucial issue that has far-reaching consequences. Fake academic certificates are becoming increasingly common, and they can undermine the integrity of the education system. In order to protect society from these fraudulent documents, digital systems can play a crucial role. One approach to detecting forged scan certificates is to use image processing techniques such as GLCM and SVD algorithms. Another approach is to use cryptography and QR codes to prevent forgery and fabrication in educational certificates and academic transcripts. The significance of detecting forgery in educational certificates is multifold. It helps to:  Ensure the authenticity of academic credentials.  Protect the integrity of the education system.  Prevent fraud and misrepresentation in job applications.  Ensure that only qualified candidates are hired for jobs.  Protect the reputation of educational institutions. 1.6.2. Expected outcome, Scope and Limitation of the study The expected outcome of detecting forgery in educational certificates is to ensure the authenticity of academic credentials, protect the integrity of the education system, prevent fraud and misrepresentation in job applications, ensure that only qualified candidates are hired for jobs, and protect the reputation of educational institutions. Scope: This study will focus on developing and evaluating a system for detecting forged educational certificates using a combination of deep learning and explainable AI (XAI) techniques. Deep Learning Model Development:  o The study will explore and train deep learning models, likely convolutional neural networks (CNNs) or transformers, to classify educational certificates as genuine or forged. o Data for training and testing the model will be collected from a defined source (e.g., educational institutions, public datasets). The scope will encompass various types of educational certificates (e.g., diplomas, degrees) but may be limited to a specific region or issuing body depending on data availability. -5- Explainable AI Integration:  The study will integrate XAI techniques like LIME or SHAP to make the deep o learning model's decision-making process interpretable. The focus will be on explaining the model's predictions for individual certificate o images, highlighting the regions of interest (ROIs) that influence its classification (genuine/forged). Evaluation and Analysis:  The model's performance will be evaluated using standard metrics like accuracy, o precision, recall, and F1-score on a separate testing dataset. The effectiveness of the XAI techniques in providing human-interpretable o explanations for the model's decisions will be assessed. Limitations:  Data Availability: The quality and quantity of training data significantly impact the model's performance. The study's scope might be limited by the availability of a diverse and comprehensive dataset of genuine and forged educational certificates.  Forgery Techniques: The model's ability to detect forgeries will depend on the types of forgeries included in the training data. The scope might not encompass all possible forgery techniques, such as sophisticated manipulations undetectable by the chosen deep learning architecture.  Explainability Granularity: XAI techniques offer insights into the model's decisions, but the level of detail and interpretability might be limited. The study might not achieve perfect human-like explanations for every prediction.  Generalizability: The model's performance might vary depending on the format and layout of certificates from different institutions. The scope might be limited to a specific type or region of educational certificates, requiring further adaptation for broader application. Additional Considerations:  This study focuses on the technical aspects of forgery detection. Ethical considerations regarding data privacy, potential biases in the training data, and the responsible use of such a system will need further exploration.  The study lays the groundwork for a practical system. Integrating the model with existing verification processes and user interface development would fall outside the immediate scope but could be addressed in future work. -6- CHAPTER TWO 2. Literature Review 2.1. Related works Educational certificates are vital documents used for academic verification and career opportunities. Unfortunately, there's an increasing problem with forged certificates, where individuals tamper with the content to misrepresent their qualifications. Detecting Forgery in Educational Certificates using Deep Learning involves utilizing the power of artificial neural networks, specifically deep learning models, to automatically identify fake or altered educational certificates. The application of deep learning for document forgery detection has gained significant traction in recent years. However, incorporating explainable AI (XAI) techniques into such systems is a relatively new area of exploration. Deep Learning for Document Forgery Detection: V. Christlein et al. (2018): This work explores Convolutional Neural Networks (CNNs) for handwritten signature forgery detection. They achieve high accuracy but acknowledge the "black-box" nature of deep learning models and call for explainability techniques. Y. Liu et al. (2020): This paper investigates CNNs for diploma forgery detection in China. They achieve promising results but emphasize the need for interpretability to gain trust from users. S. Roy et al. (2021): Here, the authors propose a framework using transfer learning with pretrained CNNs for various document forgery detection tasks. Their work highlights the importance of explainability for real-world applications. Explainable AI (XAI) for Document Analysis Tasks: M. Tessler et al. (2020): This work investigates LIME (Local Interpretable Model-Agnostic Explanations) for explaining decisions made by deep learning models in document image classification tasks. Their findings demonstrate the potential of LIME for understanding these models. -7- A. Romero et al. (2018): The authors explore SHAP (SHapley Additive exPlanations) for explaining predictions made by deep models on text classification tasks. Their work suggests the applicability of SHAP for various document analysis problems. J. Xu et al. (2021): This paper proposes a combined approach using LIME and Grad-CAM (Gradient-weighted Class Activation Mapping) for explaining forgeries in historical handwritten documents. Their work showcases the benefits of combining multiple XAI techniques. Explainable AI in Educational Credential Verification: While research directly combining explainable AI, deep learning, and educational certificate forgery detection is limited, there's growing interest in explainability for AI-powered credential verification systems. For instance, the work by A. Greenhalgh et al. (2021) explores explainability for AI-based diploma verification, highlighting the importance of transparency and trust in such systems. The application of Explainable AI (XAI) and deep learning for detecting forged documents, including educational certificates, is a growing area of research. Deep Learning for Document Forgery Detection: Xu et al. (2020): This study proposes a Convolutional Neural Network (CNN) based approach for detecting forgeries in ID cards. Their model achieved high accuracy in identifying manipulated regions of the document. Feng et al. (2019): This research explores the use of Recurrent Neural Networks (RNNs) for analyzing text inconsistencies and layout irregularities in forged certificates. Their approach demonstrated promising results for textual forgery detection. Hou et al. (2018): This work investigates the application of deep learning for forgery detection in various document types, including certificates. They highlight the importance of using large and diverse datasets to train effective models. Explainable AI (XAI) for Document Analysis: Singh et al. (2022): This research explores techniques for explaining the decision-making process of deep learning models used for document classification tasks. Their work highlights the importance of XAI for building trust and understanding in such systems. -8- Zhang et al. (2021): This study proposes an XAI approach specifically designed for image classification tasks, which can be applied to interpreting forgery detection models that rely on image analysis of certificates. Liu et al. (2020): This work explores various XAI methods for explaining deep learning models used in anomaly detection tasks. Detecting forged certificates can be framed as an anomaly detection problem, and these techniques can be adapted for this specific use case. Limitations and Considerations:  Generalizability of Deep Learning Models: Deep learning models can be sensitive to data variations. It's crucial to train the model on a dataset that reflects the specific characteristics of educational certificates issued in Oromia to ensure generalizability and avoid biases.  Interpretability vs. Accuracy: There might be a trade-off between achieving high accuracy and obtaining perfectly interpretable explanations from the deep learning model. XAI techniques can help find a balance between these two aspects.  Integration with Workflow: Consider how the Explainable AI and deep learning system will be integrated into existing verification processes for optimal workflow efficiency and user adoption. By understanding the existing research and addressing potential limitations, your project can leverage the strengths of Explainable AI and deep learning to develop a robust and userfriendly system for detecting forged educational certificates within the system. 2.2. Literature Review on Detecting Forgery Educational Certificate Explainable Artificial Intelligence (XAI) has gained significant attention due to the increasing use of AI systems across various industries, including education. In educational settings, building trust and ensuring the efficacy of AI systems require transparency and interpretability. Detecting forgery in educational certificates is a critical task, and XAI plays a crucial role in achieving this. Explainable AI holds promise in detecting forgery in educational certificates. By enhancing transparency and interpretability, we can ensure the integrity of educational credentials. Challenges of XAI in Education 1. Complexity of AI Algorithms: AI algorithms can be intricate and difficult to understand. XAI aims to make these algorithms more transparent and interpretable. -9- 2. Necessity for Transparency: In educational contexts, decisions made by AI systems can significantly impact students’ learning outcomes and prospects. Transparency is essential to build trust and accountability. Solutions and Approaches 1. Human-AI Collaboration: Researchers have explored collaborative approaches where AI systems work alongside humans. This ensures that decisions are explainable and can be validated. 2. Explainability Techniques: Various methods, such as feature importance analysis, attention mechanisms, and rule-based models, enhance the interpretability of AI systems. 3. Ethical and Legal Frameworks: Establishing guidelines and regulations for AI use in education ensures responsible and transparent practices. Competencies and Skills 1. Student and Educator Training: Developing competencies and skills to interact effectively with AI is crucial. Educators and students should understand how AI systems function and interpret their decisions. 2. Impact on Politics and Government: XAI has implications beyond education. Policymakers need to consider the ethical and legal aspects of AI adoption. Educational certificates are crucial documents that verify academic achievements and qualifications. However, the growing importance of these certificates has unfortunately led to an increase in forgery attempts. This literature review explores various methods employed to detect forged educational certificates. Traditional Techniques:  Visual Inspection: This initial method relies on trained personnel to identify inconsistencies in formatting, fonts, seals, watermarks, and printing quality. While effective for some forgeries, it can be time-consuming and susceptible to human error.  Ultraviolet (UV) Light Detection: Many genuine certificates contain security features invisible to the naked eye but detectable under UV light. However, forgers can replicate these features, limiting their effectiveness. - 10 - Automated Techniques: The rise of digital documents and readily available editing tools necessitates automated forgery detection methods.  Image Processing Techniques: o Color Analysis: Statistical analysis of color distribution can identify inconsistencies suggestive of forgery. o Font Analysis: Features like font type, size, and kerning (spacing between characters) can be compared against a database of legitimate fonts used by specific institutions. o Texture Analysis: Techniques can detect inconsistencies in the texture of the paper or printing, potentially revealing modifications.  Machine Learning (ML) and Deep Learning (DL): These techniques offer powerful tools for automated forgery detection. o Supervised Learning: Models are trained on datasets of genuine and forged certificates. During testing, the model analyzes unseen certificates and classifies them as genuine or forged. o Deep Learning: More complex neural networks can learn intricate features from image data, potentially achieving higher accuracy than traditional ML methods. Challenges and Considerations:  Evolving Forgery Techniques: As detection methods improve, forgers develop new techniques to bypass them. Continuous adaptation of detection algorithms is essential.  Data Availability: Training accurate ML and DL models requires a large and diverse dataset of both genuine and forged certificates. Acquiring such data can be challenging due to privacy concerns and the difficulty in obtaining verified forged examples.  Explainability of AI Models: While powerful, black-box AI models can be difficult to interpret. Understanding why a model classifies a certificate as forged is crucial for building trust in the system. - 11 - Detecting forged educational certificates is a pressing concern in today's globalized world, impacting individuals, institutions, and society as a whole. This review aims to provide an overview of existing research in this field, highlighting key approaches, findings, and challenges. A Machine learning approaches show promising results, with some studies reporting accuracy exceeding 95% is one of the key findings. Combining multiple modalities, such as text analysis, image analysis, and metadata checks, can further improve detection performance. Open-source datasets and collaborative research efforts are also crucial for accelerating progress and addressing evolving forgery techniques. Challenges and Future Directions are bias and fairness, ethical considerations and ability to new forgery techniques. Training data and algorithms need careful curation to avoid biases against specific institutions or groups. Balancing data privacy with effective verification requires transparent and secure data handling practices. Detection systems need to be continuously updated to remain effective against evolving threats. Detecting forged educational certificates requires a multi-pronged approach. While traditional methods still play a role, emerging technologies like machine learning and blockchain offer promising solutions for achieving greater accuracy, efficiency, and transparency. Addressing the challenges related to bias, ethics, and adaptability is crucial for ensuring the long-term effectiveness of these solutions. Continued research and collaboration among researchers, institutions, and policymakers are essential in protecting the integrity of academic credentials and upholding the value of genuine qualifications. - 12 - 2.2.1. Summary of reviewing related works Author(s) and Title Methodology (Algorithms) Experiment Result Research Questions Research Gap Mrs. G. Chandra Praba, E. Jeevitha, A. Abitha, A. Abitha, A. Abitha. “Fake Education Document Detection using Image Processing and Deep Learning” - QR-code scanner - image processing techniques Combination of Image Processing works together very efficiently and the results obtained are accurate Less effective in countering the danger of faking identity documents in the current technology Ali, Syed Sadaf, Iyyakutti Iyappan Ganapathi, Ngoc-Son Vu, Syed Danish Ali, Neetesh Saxena, and Naoufel Werghi. “Image Forgery Detection Using Deep Learning by Recompressing Images” Convolutional neural networks (CNNs) Isizoh A.N., Anyi D.O., Onyeyili T.C., Ebih U.J., Ejimofor I.A. “Certificate Fraud Detection Using Artificial Intelligence Technique” Self defining equations and modeling diagrams - The proposed technique can efficiently detect image splicing and copy-move types of image forgeries. - The experiment results are encouraging, with an overall validation accuracy of 92.23%. The result recorded achieved a Mean Square Error (MSE) performance of 0.000100Mu and Regression value of R= 0.99373 which is very good, with implication that the new system is very reliable. What are the most effective methods to detect forgery in official documents? How do these methods compare to each other in terms of accuracy and efficiency? What are the most effective methods to detect unseen forgeries in an image? How does the proposed system compare with other methods in terms of detecting unseen forgeries in an image? The lack of generalizability of existing certificate verification systems. The limitations of conventional systems and the need for automated solutions. Kethepalli Mallikarjuna, Pushan Kumar Dutta, N. Sateesh Kedarnath Kumar. “Detecting Forged Scan of Educational Certificates Using a New Feature Set Matching Algorithm” Image processing algorithm How can deep learning-based systems be improved to detect a wider range of image forgeries? How does the proposed system compare with other methods in terms of detecting unseen forgeries in an image? How can we enhance trustworthiness and authenticate images by detecting forged scan certificates using feature point matching algorithm and adaptive segmentation? - 13 - - Image forgery localization - Lack of performance in terms of accuracy and reduce their time complexity The research paper does not mention any research gap. It is possible that the authors did not consider the need for a research gap in their study. Author(s) and Title Methodology (Algorithms) Experiment Result Research Questions Research Gap Zanardelli, M., Guerrini, F., Leonardi, R. et al. Image forgery detection: a survey of recent deep-learning approaches. Multimed Tools Appl 82, 17521–17566 (2023). https://doi.org/10.1007/s11042-02213797-w Abhishek, Jindal, N. “Copy move and splicing forgery detection using deep convolution neural network, and semantic segmentation”. Multimed Tools Appl 80, 3571–3599 (2021). https://doi.org/10.1007/s11042-02009816-3 Sharma, P., Kumar, M. & Sharma, H. Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation. Multimed Tools Appl 82, 18117–18150 (2023). https://doi.org/10.1007/s11042-02213808-w Image forgery detection tools A survey of image forgery detection methods based on deep learning. How can deep learning techniques improve the detection and localization of image forgeries? The lack of a comprehensive and reliable method to detect forged certificates that are created using image processing tools such as Photoshop. Deep convolution neural network, and semantic segmentation Detecting and localizing image forgeries using deep convolution neural network and semantic segmentation. How can deep convolution neural network and semantic segmentation be used to detect and localize copy-move and splicing image forgeries with high accuracy and efficiency? Lack of Improved hybrid algorithm that uses color illumination, deep convolution neural network, and semantic segmentation to detect and localize image forgeries Photoshop, Corel Paint Shop, PhotoScape, PhotoPlus, GIMP, Pixelmator and GAN approaches Advanced image forensics should incorporate more advanced methodologies that minimize executable time and operational cost What are the different types of image forgery and how can they be detected? What are the challenges and limitations of deep learning-based image forgery detection methods? What are the future directions and open problems in image forgery detection research? The paper does not provide a comprehensive comparison of the performance, robustness, and limitations of different image forgery detection methods on various datasets & scenarios. The paper does not address the challenges posed by more advanced and realistic image forgeries, such as deepfakes, face swapping, and style transfer, which can evade conventional and deep learningbased methods. The paper does not explore the potential of combining multiple modalities, such as audio, video, and text, to enhance the image forgery detection and localization capabilities. The paper does not investigate the ethical, legal, and social implications of image forgery detection and prevention, such as privacy, consent, accountability, and trust. - 14 - Author(s) and Title Methodology (Algorithms) Experiment Result Research Questions Research Gap Ankit Katiyar and Dr. Arnav Bhavsar. “Image Forgery Detection with Interpretability” (2022). https://arxiv.org/abs/2202.00908, https://doi.org/10.48550/arXiv.2202.0 0908 Convolutional neural network (CNN) architecture How can a learning-based method using convolutional neural network (CNN) architecture detect copy-move and inpainting-based image forgeries with high accuracy and interpretability? The need for more effective and reliable methods that can detect and localize different types of image forgeries, such as copy-move and inpainting-based forgeries, with high accuracy and interpretability I. Psychoula, A. Gutmann, P. Mainali, S. H. Lee, P. Dunphy and F. Petitcolas, "Explainable Machine Learning for Fraud Detection," in Computer, vol. 54, no. 10, pp. 4959, Oct. 2021, doi: 10.1109/MC.2021.3081249. Explainability methods The proposed learning-based method, which focuses on the convolutional neural network architecture, can detect both copy-move forgeries and inpaintingbased forgeries with high accuracy and efficiency. Nearest neighbor samples produced the most informative and consistent explanations, while random and stratified samples were less reliable and more variable. The need for more effective and reliable methods that can detect and prevent different types of financial fraud, such as credit card fraud, insurance fraud, identity theft, and money laundering, with high accuracy and interpretability Shaik, Cheman. “Preventing Forged and Fabricated Academic Credentials using Cryptography and QR Codes”. International Journal of Computer Science, Engineering and Applications (IJCSEA), vol. 11, no. 1, Feb. 2021, pp. 1-10, Cryptography and QR codes How does the choice of background dataset affect the explainability of machine learning models for fraud detection? What are the trade-offs between explainability and performance of supervised and unsupervised models for fraud detection? How can explainability methods be integrated into the fraud detection pipeline to provide actionable insights for decision makers? How can cryptography and QR codes be used to detect forgery and fabrication in educational certificates and academic transcripts? What are the requirements and challenges of implementing the proposed method in educational institutes and universities? How can the proposed method be evaluated and compared with other methods of verifying academic credentials? The paper does not present any experimental results on the proposed method of preventing forged and fabricated academic credentials using cryptography and QR codes. - 15 - The motivation for developing this method of detecting forgery and fabrication in educational certificates and transcripts is not clearly stated 2.2.2. Gap Analysis To identify a gaps in any literature review we have to consider the following points:  Lack of interpretability: Explainable AI models are designed to provide transparency and interpretability to the decision-making process of AI systems. However, the lack of interpretability in some models can make it difficult to understand how they work and identify the reasons behind their decisions. This can be a significant challenge in detecting forgery educational certificates.  Limited scope: Most existing techniques for detecting forgery educational certificates are limited in scope and can only detect specific types of forgery. This can be a significant limitation in detecting new and unseen types of forgery.  Data quality: The quality of data used to train AI models can significantly impact their performance. In the case of detecting forgery educational certificates, the quality of data used to train the model can affect its ability to detect new and unseen types of forgery.  Evaluation of techniques: The evaluation of existing and new techniques for detecting forgery educational certificates is essential to determine their effectiveness. However, the lack of standardized evaluation metrics and datasets can make it challenging to compare the performance of different techniques.  Human factors: Human factors such as cognitive biases, social and cultural norms, and ethical considerations can also impact the effectiveness of AI systems in detecting forgery educational certificates. For example, human biases can affect the quality of data used to train the model, leading to inaccurate results There is a need for a technique that can efficiently and accurately detect the presence of unseen forgeries in an image. Lack of Improved hybrid algorithm that uses color illumination, deep convolution neural network, and semantic segmentation to detect and localize image forgeries. The need for more effective and reliable methods that can detect and localize different types of image forgeries, such as copy-move and inpainting-based forgeries, with high accuracy and interpretability. The lack of generalizability of existing certificate verification systems. - 16 - The motivation for developing this method of detecting forgery and fabrication in educational certificates and transcripts is not clearly stated. The limitations of conventional systems and the need for automated solutions. The research paper does not mention any research gap. It is possible that the authors did not consider the need for a research gap in their study. The paper does not provide a comprehensive comparison of the performance, robustness, and limitations of different image forgery detection methods on various datasets & scenarios. The paper does not address the challenges posed by more advanced and realistic image forgeries, such as deepfakes, face swapping, and style transfer, which can evade conventional and deep learningbased methods. The paper does not explore the potential of combining multiple modalities, such as audio, video, and text, to enhance the image forgery detection and localization capabilities. The paper does not investigate the ethical, legal, and social implications of image forgery detection and prevention, such as privacy, consent, accountability, and trust. The lack of a comprehensive and reliable method to detect forged certificates that are created using image processing tools such as Photoshop. - 17 - CHAPTER THREE 3. Methodology 3.1. Research Methods The methodology is the process of collecting data, analyzing it, selecting algorithms, and interpreting it in order to achieve the goals and answer the research questions. It is the science of studying how research is conducted methodically. We discussed research methods for detecting forgery educational certificates. It involves a combination of traditional and advanced methods to ensure the authenticity of certificates. Some of the methodologies used in detecting forgery educational certificates: Visual Inspection: The first step in detecting forgery educational certificates is visual inspection. This involves examining the certificate for any visible signs of forgery, such as blurry or low-quality printing, misspellings, or inconsistencies in the design. Authentication Markers: Educational institutions can include authentication markers, such as holograms, watermarks, or security threads, on their certificates to make them more difficult to forge. These markers can be difficult to replicate, and their presence can indicate that the certificate is genuine. Machine Learning Algorithms: Machine learning algorithms can be trained to detect patterns and anomalies in educational certificates, making it easier to identify forgeries. These algorithms can analyze various features, such as the font, color, and layout, to determine the authenticity of a certificate. Document Analysis: Document analysis is a scientific method used to examine the authenticity of documents. This method involves analyzing the paper, ink, and other materials used in the certificate to determine its authenticity. Database Verification: Educational institutions can maintain a database of all issued certificates. This database can be used to verify the authenticity of certificates by comparing the certificate in question with the one in the database. Third-Party Verification: Many institutions outsource the verification process to third-party services. These services use a combination of traditional and advanced methods to verify the authenticity of educational certificates. By using a combination of these methodologies, educational institutions can ensure the authenticity of their certificates and prevent fraudulent claims. - 18 - 3.2. Research Design Methodological Steps: 1. Data Collection: Gathering a large and diverse dataset of both genuine and forged certificates is crucial. Different types of forgeries and various university documents need to be represented. 2. Data Preprocessing: The dataset is cleaned, standardized, and formatted for machine learning algorithms. This might involve image resizing, text extraction, and feature engineering. 3. Model Training and Selection: Different ML models, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), are trained on the prepared data. Their performance is evaluated and compared to select the most accurate and efficient model. 4. Feature Extraction and Identification: The chosen model analyzes specific features within the certificates, such as font inconsistencies, unusual word patterns, paper grain, or pixel-level discrepancies, to identify signs of forgery. 5. Decision Making and Result Generation: The model outputs a prediction based on the extracted features, classifying the certificate as genuine or forged. This output can be presented with a confidence score indicating the certainty of the prediction. 3.3. Materials Explainable AI (XAI) plays a crucial role in detecting forged educational certificates using artificial intelligence. Black Box Problem: Traditional AI models, often referred to as "black boxes," can achieve high accuracy in detecting forgeries. However, they lack transparency in their decision-making process, making it difficult to understand how they arrive at their conclusions.  Bias and fairness: If the training data contains biases, the model may inherit them and unfairly target certain institutions or groups.  Trust and acceptance: Without understanding how the model works, institutions and individuals may hesitate to rely on its outputs. - 19 -  Debugging and improvement: Identifying and addressing false positives or weaknesses in the model becomes challenging without insights into its reasoning. Detecting forged educational certificates involves two main types of materials: 1. Physical Materials:  Educational certificates: These can be paper-based or digital depending on the issuing institution and timeframe. Different paper types, printing techniques, and security features (holograms, watermarks) can be analyzed.  Reference documents: Genuine certificates from the issuing institution serve as a baseline for comparison. Additional reference materials like historical records, official templates, and security feature guidelines might be used.  Specialized equipment: Depending on the chosen detection methods, tools like high-resolution scanners, microscopes, ultraviolet lamps, and forensic analysis equipment might be necessary. 2. Data and Information:  Images of certificates: Both genuine and forged certificates are scanned or photographed and processed as digital images for analysis.  Textual data: Extracted text from certificates, such as names, dates, institution details and signatures, can be analyzed for inconsistencies or unusual patterns.  Metadata: Information embedded within the document file, like timestamps, author details, and software used, can be analyzed for discrepancies or clues to forgery.  Databases: Institutional databases store issuing records and details of genuine certificates, allowing for verification and data cross-checking.  Machine learning datasets: Large datasets of both genuine and forged certificates are crucial for training and optimizing machine learning models used for automated detection.  Research and technical documentation: Academic publications, guidelines and technical reports on forgery detection techniques and technologies provide valuable knowledge and reference material. The specific materials used will depend on the chosen detection methods, available resources, and the types of forgeries being targeted. - 20 - By effectively utilizing and managing both physical and digital materials, we can develop more comprehensive and robust solutions for detecting forged educational certificates and upholding the integrity of academic credentials. 3.4. Procedures Detecting forged educational certificates requires a systematic procedure, typically involving a combination of traditional methods and advanced technologies. The breakdown of the key steps are: 1. Data Acquisition and Preparation:  Gather a diverse dataset: Collect genuine and forged certificates representing various institutions, forgery techniques, and printing styles.  Preprocess the data: Standardize formats, extract relevant features (text, images, metadata), and clean the data for accurate analysis. 2. Feature Analysis and Selection:  Identify key features: Analyze visual elements (fonts, signatures, paper texture), textual patterns (inconsistencies, unusual formatting), and metadata discrepancies to distinguish forged certificates.  Select informative features: Choose features that contribute most to accurate forgery detection while considering efficiency and processing limitations. 3. Model Training and Optimization:  Choose a suitable ML model: Depending on the type of features and desired accuracy, select an appropriate machine learning model, such as Convolutional Neural Networks (CNNs) for image analysis or Recurrent Neural Networks (RNNs) for textual data.  Train and refine the model: Train the model on the prepared data, evaluate its performance, and adjust parameters or features for optimal accuracy and generalization. 4. Forgery Detection and Verification:  Analyze individual certificates: Apply the trained model to new certificates, extracting features and predicting the probability of forgery.  Combine with traditional methods: Integrate visual inspection, document analysis, and database verification alongside the model's output for comprehensive assessments. 5. Decision Making and Result Presentation: - 21 -  Set a threshold: Determine a confidence level above which a certificate is classified as forged based on the model's prediction score.  Present results: Report the forgery probability, highlight suspicious features, and provide explanations (if using XAI techniques) for transparency and trust. 6. Continuous Improvement and Monitoring:  Update the model: As new forgery techniques emerge, retrain the model on expanded datasets to maintain its effectiveness.  Monitor performance: Track false positives and negatives, analyze trends, and adapt the system to address evolving threats. 3.5. Evaluation Evaluating the effectiveness of systems for detecting forged educational certificates is crucial to ensure their accuracy, reliability, and fairness. Some key metrics and approaches to consider: Performance metrics:  Accuracy: The percentage of correctly classified certificates, both genuine and forged.  Sensitivity: The rate of correctly identified forged certificates (True Positive Rate).  Specificity: The rate of correctly identified genuine certificates (True Negative Rate).  Precision: The proportion of identified forgeries that are truly forged (Positive Predictive Value).  False Positive Rate (FPR): The rate of genuine certificates misclassified as forged.  False Negative Rate (FNR): The rate of forged certificates misclassified as genuine. Evaluation methods:  Hold-out validation: Split the data into training, validation, and test sets and evaluate the system's performance on the unseen test set.  K-fold cross-validation: Repeat the training and validation process multiple times with different data partitions for a more robust evaluation.  AUC-ROC curve: Visualizes the trade-off between sensitivity and specificity, aiding in selecting an optimal operating point for the system.  Confusion matrix: Summarizes the classification results, facilitating analysis of specific types of errors. - 22 -  Human-in-the-loop evaluation: Combine automated detection with human expertise for complex cases or in security-sensitive settings. Challenges in evaluation:  Limited availability of ground truth data: Obtaining sufficient and diverse datasets of both genuine and forged certificates can be difficult.  Evolving nature of forgery techniques: Systems need to adapt to detect new and increasingly sophisticated methods of forgery.  Subjectivity and human error: Manual evaluation methods can be unreliable and inconsistent. 3.6. Limitations Detecting forgery in educational certificates can be a complex task, and there are several limitations associated with the process. Some common limitations to consider are the followings: 1. Evolving techniques: Forgers continuously improve their techniques to make their forgeries more convincing. They may use advanced printing technologies, highquality materials, or even replicate security features accurately. As a result, traditional detection methods may become less effective against sophisticated forgeries. 2. Lack of standardized security features: Educational institutions may vary in the security features they incorporate into their certificates. Some institutions may have robust security measures, such as holograms, watermarks, or UV-reactive inks, while others may have limited or no security features at all. This lack of standardization makes it challenging to develop a universal detection method. 3. Availability of genuine certificates: Obtaining genuine certificates for reference purposes can be difficult, especially if they are from different institutions or if the issuing process is complicated. Without genuine certificates as a point of comparison, it becomes harder to identify discrepancies in suspicious documents accurately. 4. Limited access to advanced equipment: Some detection techniques, such as advanced forensic analysis or specialized equipment, may require expertise and resources that are not readily available to everyone. Access to tools like high- - 23 - resolution microscopes, UV light sources, or spectroscopy equipment may be limited, making it challenging to conduct in-depth examinations. 5. Time and cost constraints: Thoroughly examining each certificate for potential forgery can be a time-consuming process, particularly when dealing with a large volume of documents. Additionally, employing experts or specialized services to assist with forgery detection can be costly, making it impractical for some institutions or organizations. 6. Human error and subjectivity: The process of detecting forgery in educational certificates often relies on the judgment and expertise of individuals examining the documents. However, human error and subjectivity can introduce biases or overlook subtle signs of forgery, leading to false positives or false negatives. 3.7. Conceptual framework A conceptual framework is a set of ideas, assumptions, and principles that guide the design and implementation of a research project or a system. In the context of detecting forgery in educational certificates, a conceptual framework may include the following elements:  The problem statement: This defines the scope and motivation of the research or system, such as the prevalence and impact of forged certificates, the challenges and limitations of existing methods, and the objectives and expected outcomes of the proposed solution.  The literature review: This provides a comprehensive and critical analysis of the relevant existing works, such as the methods, techniques, tools, and applications that have been used or proposed for detecting forgery in educational certificates. This also identifies the gaps and limitations of the current state-ofthe-art and the opportunities for improvement and innovation.  The methodology: This describes the design and implementation of the proposed solution, such as the data sources, the algorithms, the models, the evaluation metrics, and the experiments that are used or developed for detecting forgery in educational certificates. This also explains the rationale and justification of the chosen methods and techniques and how they address the research questions or objectives.  The results and discussion: This presents and analyzes the findings and outcomes of the proposed solution, such as the performance, accuracy, efficiency, - 24 - and limitations of the methods and models for detecting forgery in educational certificates. This also compares and contrasts the results with the existing works and discusses the implications and contributions of the research or system.  The conclusion and future work: This summarizes the main points and achievements of the research or system, such as the problem statement, the literature review, the methodology, and the results and discussion. This also provides recommendations and suggestions for future research or development, such as the limitations, challenges, and open issues that need to be addressed or explored further. A conceptual framework for detecting forged educational certificates helps us organize and understand the key components and processes involved in addressing this complex challenge. 1. Inputs:  Physical/Digital Certificates: Both genuine and forged certificates in various formats (paper, digital scans, images) serve as the raw data for analysis.  Supporting Data: Additional information like historical records, institutional guidelines, security feature specifications, and known forgery techniques can enrich the detection process.  Preprocessing and Standardization: Raw data undergoes cleaning, formatting, and feature extraction to prepare it for analysis. 2. Detection Techniques:  Traditional Methods: Visual inspection, document analysis, and database verification remain vital initial steps, offering human expertise and direct validation.  Machine Learning: Supervised and unsupervised learning algorithms trained on large datasets of genuine and forged certificates can analyze textual, visual, and metadata features to identify anomalies and inconsistencies.  Blockchain Technology: Securely storing and verifying certificate data on a tamper-proof blockchain can enhance transparency and authenticity but might not be directly involved in forgery detection. 3. Feature Analysis and Selection:  Visual Features: Font inconsistencies, paper properties, security element analysis, and image-level texture comparisons can reveal signs of forgery. - 25 -  Textual Features: Unusual word patterns, formatting irregularities, inconsistencies in dates and names, and extracted metadata can provide clues.  Metadata Features: Embedding timestamps, author details, and software information within the certificate file can be analyzed for discrepancies. 4. Decision Making and Output:  Model Classification: Based on extracted features and analysis, the system classifies the certificate as genuine, forged, or suspicious requiring further investigation.  Confidence Levels: Providing a confidence score alongside the classification allows for nuanced judgments and understanding the certainty of the prediction.  Explainability and Transparency: Employing Explainable AI (XAI) techniques can offer insights into the model's reasoning behind its decision, promoting trust and understanding. 5. Feedback and Refinement:  Performance Monitoring: Evaluating the system's accuracy, sensitivity, and other metrics on new data is crucial to identify weaknesses and areas for improvement.  Adaptive Updating: Continuously updating the model with new data and evolving forgery techniques ensures its effectiveness against emerging threats.  Human-in-the-Loop: Combining automated detection with human expertise can address complex cases, refine interpretation, and maintain human oversight. 6. Societal Context and Considerations:  Ethical Implications: Addressing potential biases in data and algorithms, ensuring data privacy, and mitigating discriminatory outcomes are essential ethical considerations.  Legal and Regulatory Frameworks: Clear legal frameworks for using detection systems, defining forgery, and establishing consequences for illegal activities are crucial.  Collaboration and Sharing: Fostering collaboration and data sharing among institutions, researchers, and authorities can enhance detection capabilities and combat global forgery challenges. - 26 - CHAPTER FOUR 4. Work and Budget Plan 4.1. Work plan A work plan is a document that outlines the objectives, activities, timeline, and expected outcomes of a project or a task. In the context of detecting forged educational certificates, a work plan may include the following elements:  The background: This provides the context and motivation of the project or task, such as the problem statement, the research questions, the literature review, and the conceptual framework of detecting forged educational certificates.  The objectives: This defines the specific and measurable goals of the project or task, such as the expected outputs, outcomes, and impacts of detecting forged educational certificates.  The activities: This describes the steps and actions that will be taken to achieve the objectives, such as the data collection, the data analysis, the method development, the method evaluation, and the result dissemination of detecting forged educational certificates.  The resources: This identifies the human, financial, technical, and logistical resources that will be required or available for the project or task, such as the team members, the budget, the equipment, the software, and the data sources of detecting forged educational certificates.  The timeline: This specifies the duration and schedule of the project or task, such as the start date, the end date, the milestones, and the deliverables of detecting forged educational certificates.  The risks and challenges: This anticipates the potential difficulties and uncertainties that may arise during the project or task, such as the data quality, the data availability, the method validity, the method reliability, and the ethical issues of detecting forged educational certificates. This also proposes the mitigation strategies and contingency plans to address or overcome them.  The evaluation and monitoring: This defines the criteria and indicators that will be used to measure the progress and performance of the project or task, such as the quality, the accuracy, the efficiency, and the effectiveness of detecting forged educational certificates. This also describes the methods and tools that will be - 27 - used to collect and analyze the data and feedback from the stakeholders and beneficiaries of detecting forged educational certificates. Table 1: Work Plan No Time Schedule Activities Week 1 Week 2 Week 3 Week 4 Week 5 Week 3 1 Literature review Data collection and 2 preprocessing 3 Feature extraction Neural network architecture 4 design and implementation 5 Training and validation 6 Post-processing Real-world application 7 evaluation 8 Analysis and reporting 4.2. Budget plan A budget plan is a document that outlines the objectives, activities, timeline, and expected outcomes of a project or a task. No Items Measurement 1 Pen PCS 2 Dataset Kb/Mb/Gb 3 Hardware and software PCS 4 Travel and accommodation Quantity Cost / birr 15 Total cost 25 375 2 100000 200000 Trips 3 50 150 5 Publication fees Pages 4 200 800 6 Miscellaneous expenses Hours 20 5.75 115 Total Cost 201440 - 28 - Data based 4.3. Future Work Detecting forged educational certificates is an ongoing battle against ever-evolving techniques. While current solutions are making strides, several promising avenues offer exciting possibilities for future advancements: Technological Advancements:  Deep learning advancements: Exploring new neural network architectures and unsupervised learning techniques can potentially tackle complex forgeries and adapt to unseen threats.  Transfer learning and domain adaptation: Transferring knowledge from other domains (e.g., document forgery detection) can accelerate model training and improve generalization for specific certificate types.  Hybrid approaches: Combining deep learning with traditional methods like document analysis and physical property verification can improve overall accuracy and reliability.  Biometric integration: Analyzing fingerprints, signatures, or other biometric markers embedded in certificates could offer a strong layer of authentication.  Blockchain and decentralized solutions: Securely storing and verifying certificate data on a tamper-proof blockchain can enhance transparency and prevent centralized manipulation. Focus on Explainability and Trust:  Advanced XAI techniques: Develop more comprehensive and user-friendly explanations for AI decisions, allowing better understanding and trust in the detection process.  Human-in-the-loop systems: Leverage AI for initial screening and detection, but integrate human expertise for complex cases and ethical considerations.  Standardized reporting and guidelines: Establish transparent reporting protocols for detected forgeries and consistent ethical guidelines for AI development and deployment. Collaboration and Data Sharing:  Global data pools and platforms: Building secure platforms for sharing data among institutions and countries can significantly enhance detection capabilities and combat global forgery rings. - 29 -  Collaborative research and development: Fostering open research initiatives and joint development efforts can accelerate innovation and optimize solutions for diverse contexts.  Public awareness and education: Raising awareness about the risks of using forged credentials and promoting responsible authentication practices among individuals and institutions. Addressing Societal Challenges:  Mitigating bias and discrimination: Developing fairness-aware algorithms and data cleansing practices to prevent biased outcomes in detection, particularly against specific institutions or groups.  Ensuring accessibility and affordability: Making advanced detection systems accessible and affordable for institutions with limited resources, including those in developing countries.  Legal and regulatory frameworks: Establishing clear legal frameworks for using detection systems, defining forgery offenses, and outlining consequences for illegal activities. Future Directions:  Further Research: Investigating XAI in education remains an active area of research. Researchers should explore novel techniques and evaluate their effectiveness.  Practical Implementation: Applying XAI in real-world educational systems requires collaboration between researchers, educators, and policymakers.  Hybrid Systems: Combining traditional techniques with automated methods can offer a more robust and comprehensive approach to forgery detection.  Transfer Learning: Leveraging pre-trained models on large image datasets can improve the efficiency and accuracy of forgery detection, even with limited educational certificate data. - 30 - References: Shaik, Cheman, Preventing Forged and Fabricated Academic Credentials using Cryptography and QR Codes (February 28, 2021). International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.11, No.1, February 2021, Available at SSRN: https://ssrn.com/abstract=3831729 Abazi Chaushi, B., Selimi, B., Chaushi, A., & Apostolova, M. (2023). Explainable Artificial Intelligence in Education: A Comprehensive Review. Communications in Computer and Information Science, 1902, 48–71. M. A. Ali ALAMERİ, B. Ciylan and B. Mahmood, "Computational Methods for Forgery Detection in Printed Official Documents," 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), Manama, Bahrain, 2022, pp. 307-313, doi: 10.1109/ICETSIS55481.2022.9888875. P. Yang, W. Fang, F. Zhang, L. Bai and Y. Gao, "Document Image Forgery Detection Based on Deep Learning Models," 2022 International Symposium on Electrical, Electronics and Information Engineering (ISEEIE), Chiang Mai, Thailand, 2022, pp. 36-41, doi: 10.1109/ISEEIE55684.2022.00014 Zanardelli, M., Guerrini, F., Leonardi, R. et al. Image forgery detection: a survey of recent deep-learning approaches. Multimed Tools Appl 82, 17521–17566 (2023). https://doi.org/10.1007/s11042-022-13797-w Ali, Syed Sadaf, Iyyakutti Iyappan Ganapathi, Ngoc-Son Vu, Syed Danish Ali, Neetesh Saxena, and Naoufel Werghi. 2022. "Image Forgery Detection Using Deep Learning by Recompressing Images" Electronics 11, no. 3: 403. https://doi.org/10.3390/electronics11030403 M. Patel, K. Rane, N. Jain, P. Mhatre and S. Jaswal, "Image Forgery Detection using CNN," 2023 3rd International Conference on Intelligent Technologies (CONIT), Hubli, India, 2023, pp. 1-4, doi: 10.1109/CONIT59222.2023.10205377. Kethepalli Mallikarjuna, Pushan Kumar Dutta, N. Sateesh Kedarnath Kumar. “Detecting Forged Scan of Educational Certificates Using a New Feature Set Matching Algorithm”. International Journal of Tomography & Simulation, 2019. http://www.ceser.in/ceserp/index.php/ijts/article/view/5899 C. N. Bharti and P. Tandel, "A survey of image forgery detection techniques," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, 2016, pp. 877-881, doi: 10.1109/WiSPNET.2016.7566257. Zanardelli, M., Guerrini, F., Leonardi, R. et al. Image forgery detection: a survey of recent deeplearning approaches. Multimed Tools Appl 82, 17521–17566 (2023). https://doi.org/10.1007/s11042-022-13797-w Sharma, P., Kumar, M. & Sharma, H. “Comprehensive analyses of image forgery detection methods from traditional to deep learning approaches: an evaluation”. Multimed Tools Appl 82, 18117–18150 (2023). https://doi.org/10.1007/s11042-022-13808-w Ankit Katiyar and Dr. Arnav Bhavsar. Indian Institute of Technology Mandi, HP, INDIA. “Image Forgery Detection with Interpretability” (2022). https://arxiv.org/abs/2202.00908, https://doi.org/10.48550/arXiv.2202.00908 - 31 -