DEEP: Extending the Digital Forensics Process Model for Criminal Investigations

Collie, Jan and Overill, Richard E. (2020). DEEP: Extending the Digital Forensics Process Model for Criminal Investigations. Athens Journal of Sciences, 7(4) pp. 225–240.




The importance of high quality, reliable forensic analysis –an issue that is central to the delivery of justice– has become a topic for marked debate with scientists, specialists and government bodies calling for improved standards and procedures. At the same time, Law Enforcement agencies are under pressure to cut the cost of criminal investigations. The detrimental impact that this has had on all forensic disciplines has been noted internationally, with the UK’s House of Lords warning that if the trend continues, crimes could go unsolved and miscarriages of justice may increase. The pivotal role that digital forensics plays in investigating and solving modern crimes is widely acknowledged: in Britain, the police estimate it features in 90% of cases. In fact, today’s law enforcement officers play a key part in the recovery, handling and automated processing of digital devices yet they are often poorly trained to do so. They are also left to interpret outputs, with the results being presented in court. This, it is argued, is a dangerous anomaly and points to a significant gap in the current, four-stage digital forensics process model (DFPM). This paper presents an extension to that model, the Digital Evidence Enhanced Process (DEEP), with the aim of fine-tuning the mechanism and ensuring that all digital evidence is scrutinised by a qualified digital forensics analyst. The consequence of adopting DEEP in actual criminal investigations will be to ensure that all digital evidence is analysed and evaluated to the highest professional and technical competency standards, resulting in the enhanced reliability of digital evidence presented in court which will serve the cause of justice in terms of reduced instances of associated unsafe convictions and/or unjustified exculpations.

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