An explainable AI-based digital forensics model for image tampering detection in Uganda’s judicial system.
| dc.contributor.author | Bageya, Ivan | |
| dc.date.accessioned | 2026-10-08T08:02:24Z | |
| dc.date.available | 2026-10-08T08:02:24Z | |
| dc.date.issued | 2026 | |
| dc.description | Dissertation | |
| dc.description.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. | |
| dc.description.sponsorship | Prof. Gilbert, Gilibrays Ocen ; Dr. Godliver, Owomugisha ; Busitema University | |
| dc.identifier.citation | Bagaye, I. (2026). An explainable AI-based digital forensics model for image tampering detection in Uganda’s judicial system. [Unpublished dissertation]. Busitema University. | |
| dc.identifier.uri | https://bdears.busitema.ac.ug/handle/123456789/10504 | |
| dc.language.iso | en | |
| dc.publisher | Busitema University | |
| dc.title | An explainable AI-based digital forensics model for image tampering detection in Uganda’s judicial system. | |
| dc.type | Other |