Artificial intelligence in mobile forensics: A survey of current status, a use case analysis and AI alignment objectives

As the capabilities and utility of Artificial Intelligence and Machine Learning systems continue to improve, they are expected to have an increasingly powerful influence in the digital forensic investigation process. The concurrent proliferation of mobile devices and rapid increase of forensic value of related artifacts creates the requirement for a comprehensive review of the current status of artificial intelligence software usage and usefulness in Mobile Forensics. In this context, we conducted a survey to evaluate the characteristics and properties of AI functions in mobile forensic software from the practitioners' perspective and enhance understanding to the work in the field. In this study, we evaluated the performance of image categorization software in digital forensics using a variety of evaluation metrics including accuracy, precision, recall, and F1-score, as well as the confusion matrix. In this research we also identify and integrate theoretical principles to conceptualize an AI Alignment framework pertaining to Mobile Forensics and Digital Forensics in general, in order to accurately determine specific AI strategy objectives and potential solutions to the current technical and administrative landscape. We emphasized the importance of interpretability and transparency in AI systems and the need for a comprehensive approach to understanding the reasoning behind the software's decisions. Additionally, we highlighted the importance of robustness in image categorization software, as well as the consideration of AI governance and standardized procedures concepts. Our results show that the accuracy and robustness of the image categorization software have a significant impact on the outcome of legal cases and that the software should be designed with interpretability, transparency, and robustness in mind. Through the examination of the survey responses, the evaluation of the image categorization software and research literature, we explore existing and potential approaches to aligned Artificial Intelligence and analyze their contribution to the forensic examination of cases.