Humans generate lots of data in the cyberworlds by their behaviors on social networks, interactions with computers or smartphones (writing documents, capturing images,…). Analyzing digital traces from Internet, hard drives or computers is an important issue considering the amount of data to process and the associated applications (criminal investigation, recruitment,…). In this paper, we propose an open-source software platform for such a task. It embeds many high-level evaluated tools. These tools rely on recent advances in artificial intelligence and deep learning. Our goal is to facilitate digital investigations conducted by criminal experts, researchers in digital archives, or IT engineers. This platform embeds many tools designed to process a document in order to extract some knowledge (as for example, determining if the filetype has been corrupted). We illustrate its benefit through the analysis of a real memory dump from a hard drive.

Cyberworlds offer a vast quantity of knowledge and services on all topics for Internet users. The protection of children is an important issue on Internet and could be solved by detecting automatically explicit content. Another application is to facilitate digital forensic experts when analyzing media such as hard drives to detect child pornography content in criminal affairs. In this work, we focus on images and we study the efficiency of existing methods from the literature mainly based on machine learning and deep learning approaches. We apply a rigorous protocol with significant datasets in order to draw conclusions on the performance we can expect in real conditions. This study shows that this task is not really solved by existing tools. Moreover, the frontier of explicit content is also not always easy to define.