Abstract

The growing spread of deepfake images, combined with the sophistication of machine learning tools and techniques used to produce them, pose serious threats to the integrity of information, individual privacy, and the preservation of public trust. To detect these deepfake images for legal investigation purposes, it requires advanced detection mechanisms that not only achieve high accuracy but also provide transparent and understandable explanations of the decisions made.This talk will present a new framework for deepfake detection, which not only pursues accuracy but, more crucially prioritises the explainability of detection, which is a critical need in legal investigations contexts such as policing and digital forensics. The framework is composed of advanced machine learning models, an explainable AI (XAI) component and commonly used image processing methods for detecting manipulations, to detect and explain manipulations in deepfake images. Four independently trained Xception models were developed for the original and processed images, and through decision fusion achieved an overall detection accuracy of 97 %. Moreover, the framework achieved an F1 score of 92 % from a hidden test dataset used in the UK Home Office’s Deepfake Detection Challenge 2024, placing it third out of the competing teams in the image deepfake category. In UK Deepfake Detection Challenge Live 2026, the team came first in the explainability category and overall 7th beating other 13 teams who were from tech companies and universities worldwide.Our work demonstrates that combining machine learning, image processing, XAI with human understandable rationales results in a demonstrably effective and practical deepfake detection system that could significantly streamline criminal investigations as performed in policing and digital forensics.