Semantic View Induction for Object-Guided Scene Structuring
Researchers propose a framework for scene structuring using semantic view induction and object evidence.
Forensics and Security Research Group
Academic cybersecurity and digital forensics research group spanning University College Dublin and South East Technological University.
Research Focus
The Forensics and Security Research Group conducts research in digital forensics, cybersecurity, network investigation, artificial intelligence for forensic workflows, cloud and IoT forensics, and digital forensic education.
Founded in University College Dublin and now expanded through collaboration with South East Technological University, the group works with academic, law-enforcement, and industry partners on research that improves the reliability, scalability, and practical impact of digital investigations.
Digital Forensics
Network Investigation
AI for Forensics
Cloud and IoT Evidence
Forensic Readiness
Education and Training
Latest
News
VAAS: Vision-Attention Anomaly Scoring for Image Manipulation Detection in Digital Forensics
VAAS: A novel dual-module framework for image manipulation detection in digital forensics, combining global attention-based anomaly estimation with patch-level self-consistency scoring for interpretable anomaly detection and tamper localization.
Objects as Universal Geolocation Cues: A Computer Vision Approach
This paper proposes a computer vision approach to geolocation using universal visual cues, specifically electrical plug sockets, to narrow down the search space for law enforcement in combating crimes such as human trafficking and child exploitation.
New DFRWS EU 2026 Publications
The group has new work appearing at DFRWS EU 2026 and in Forensic Science International: Digital Investigation.
Recent Output
Selected Publications
- 2026
Investigation of large language models, GenAI, and proprietary AI systems: Digital forensic evidence, readiness and regulationForensic Science International: Digital Investigation Vol. 57 Article 302135
- 2025
Towards a standardized methodology and dataset for evaluating LLM-based digital forensic timeline analysisForensic Science International: Digital Investigation Vol. 54S Article 301982
- 2025
