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Nowadays, the volume of the multimedia heterogeneous evidence presented for digital forensic analysis has significantly increased, thus requiring the application of big data technologies, cloud-based forensics services, as well as Machine Learning (ML) techniques. In digital forensics domain, ML algorithms have been applied for cybercrime investigation such as child abuse investigations, malware classification, and image forensics. This paper addresses this issues and deals with forensic analysis of digital images and videos. In particular, this work aims at proposing a multimedia classification tool with a parallel software architecture for a fast inspection, which is easy to use (to be used by officers during a search), requires limited hardware resources and it is built on an open-source software to limit its costs. Moreover, this tool must be able to quickly inspect multiple devices at a time. When positives are found in a device, such device will be seized for a deeper analysis later in the lab. It will not be seized otherwise, reducing the inconvenience for the suspect as well as the time required for the next analysis phase. As a case study, we focus on the identification of child pornography images. Experimental results show that the proposed architecture is capable of guaranteeing a high recall, a fast process and high performances in real scenarios.

An offline parallel architecture for forensic multimedia classification