An explainable AI-based digital forensics model for image tampering detection in Uganda’s judicial system.
No Thumbnail Available
Date
2026
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Busitema University
Abstract
The growing dependence on digital image evidence in Ugandan legal procedures has revealed a key flaw for example the lack of standardized mechanisms for ensuring image integrity and authenticity. Without cryptographic hashing, chain-of-custody procedures, or forensic tools, digital evidence is vulnerable to alteration, jeopardizing judicial outcomes and public trust. This problem is particularly serious in low-resource countries like Uganda, where technological infrastructure lags behind the increasing use of digital evidence in court.
This study sought to close this gap by creating and testing an AI-based tamper detection model specific to Uganda's forensic environment. A mixed-methods research strategy was used, which included quantitative surveys and qualitative interviews with legal professionals, forensic experts, and law enforcement officials (n=10), who confirmed the lack of formal verification procedures and voiced conditional support for AI deployment.
As a result, a convolutional neural network (CNN) model was designed and tested on a dataset of 12,617 image pairs containing legitimate and tampered images, yielding 90.3% accuracy, 83.5% precision, 94.8% recall, and an AUC of 0.969. This performance exhibits remarkable reliability in distinguishing between modified and legitimate images.
Furthermore, the model included Explainable AI (XAI) via Grad-CAM visualization to improve transparency and legal defensibility, addressing the black-box issue widespread in forensic AI applications. These findings provide a practical, evidence-based approach that connects technical feasibility and judicial reality, improving the dependability of digital evidence, promoting legal reforms, and sustaining institutional trust in Uganda's expanding digital forensics environment.
Description
Dissertation
Keywords
Citation
Bagaye, I. (2026). An explainable AI-based digital forensics model for image tampering detection in Uganda’s judicial system. [Unpublished dissertation]. Busitema University.