Explainable deepfake detection: A multi-model framework with human-interpretable rationales for legal investigation purposes

• Deepfake images pose serious threats to integrity of information and public trust • Detecting deepfakes for legal investigations require high accuracy and transparency • Our multimodal framework detects deepfake images and explains detection decision • Hidden features in deepfake images were discovered in our processed images • Our framework came third in UK Home Office’s Deepfake Detection Challenge 2024 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 paper presents 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 three commonly used image processing methods for detecting manipulations, to detect and explain manipulations in deepfake images of human faces. Four independently trained CNN 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. Shapley values were also used to identify the facial features that influenced the models’ detection decisions. This information enabled us to home in on various areas on the face to find features more likely to occur in deepfake images. Through Bayes’ theorem, we presented a human-understandable detection method, achieving 85% detection accuracy on the test images while maintaining explainability of the detection rationales. 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. Future research will explore the interplay between psychological factors and the acceptance and trust of such frameworks and extend the framework by incorporating additional image processing techniques to enhance detection accuracy.