Internet of Things (IoT) applications play a relevant role in today’s industry in sharing diagnostic data with off-site service teams, as well as in enabling reliable predictive maintenance systems. Several interventions scenarios, however, require the physical presence of a human operator: Augmented Reality (AR), together with a broad-band connection, represents a major opportunity to integrate diagnostic data with real-time in-situ acquisitions. Diagnostic information can be shared with remote specialists that are able to monitor and guide maintenance operations from a control room as if they were in place. Furthermore, integrating heterogeneous sensors with AR visualization displays could largely improve operators’ safety in complex and dangerous industrial plants. In this paper, we present a complete setup for a remote assistive maintenance intervention based on 5G networking and tested at a Vodafone Base Transceiver Station (BTS) within the Vodafone 5G Program. Technicians’ safety was improved by means of a lightweight AR Head-Mounted Display (HDM) equipped with a thermal camera and a depth sensor to foresee possible collisions with hot surfaces and dangerous objects, by leveraging the processing power of remote computing paired with the low latency of 5G connection. Field testing confirmed that the proposed approach can be a viable solution for egocentric environment understanding and enables an immersive integration of the obtained augmented data within the real scene.

The amount of multimedia content shared everyday, combined with the level of realism reached by recent fake-generating technologies, threatens to impair the trustworthiness of online information sources. The process of uploading and sharing data tends to hinder standard media forensic analyses, since multiple re-sharing steps progressively hide the traces of past manipulations. At the same time though, new traces are introduced by the platforms themselves, enabling the reconstruction of the sharing history of digital objects, with possible applications in information flow monitoring and source identification. In this work, we propose a supervised framework for the reconstruction of image sharing chains on social media platforms. The system is structured as a cascade of backtracking blocks, each of them tracing back one step of the sharing chain at a time. Blocks are designed as ensembles of classifiers trained to analyse the input image independently from one another by leveraging different feature representations that describe both content and container of the media object. Individual decisions are then properly combined by a late fusion strategy. Results highlight the advantages of employing multiple clues, which allow accurately tracing back up to three steps along the sharing chain.

Forgery operations on video contents are nowadays within the reach of anyone, thanks to the availability of powerful and user-friendly editing software. Integrity verification and authentication of videos represent a major interest in both journalism (e.g., fake news debunking) and legal environments dealing with digital evidence (e.g., courts of law). While several strategies and different forensics traces have been proposed in recent years, latest solutions aim at increasing the accuracy by combining multiple detectors and features. This paper presents a video forgery localization framework that verifies the self-consistency of coding traces between and within video frames by fusing the information derived from a set of independent feature descriptors. The feature extraction step is carried out by means of an explainable convolutional neural network architecture, specifically designed to look for and classify coding artifacts. The overall framework was validated in two typical forgery scenarios: temporal and spatial splicing. Experimental results show an improvement to the state of the art on temporal splicing localization as well as promising performance in the newly tackled case of spatial splicing, on both synthetic and real-world videos.

The capability of associating an image to its geographical location is a significant concern in journalism and digital forensics. Given the availability of geo-tagged satellite imagery for most of the Earth's surface, retrieving the location of a generic picture can be addressed as a cross-view image matching between aerial and ground views. In this paper, we outline some initial steps toward the development of a fully-unsupervised algorithm for ground-to-aerial image matching, exploiting the view-invariant adjacency relationships of the landmarks appearing in both views. We introduce a graph-based strategy that, given a set of pre-extracted landmarks, localizes the viewpoint of a ground-level 360-degree image within a broad aerial view of the same area, by matching the respective landmark graphs according to a specifically designed likelihood model.

The recent development of multimedia has made video editing accessible to everyone. Unfortunately, forensic analysis tools capable of detecting traces left by video processing operations in a blind fashion are still at their beginnings. One of the reasons is that videos are customary stored and distributed in a compressed format, and codec-related traces tends to mask previous processing operations. In this paper, we propose to capture video codec traces through convolutional neural networks (CNNs) and exploit them as an asset. Specifically, we train two CNN s to extract information about the used video codec and coding quality, respectively. Building upon these CNN s, we propose a system to detect and localize temporal splicing for video sequences generated from the concatenation of different video segments, which are characterized by inconsistent coding schemes and/or parameters (e.g., video compilations from different sources or broadcasting channels). The proposed solution is validated using videos at different resolutions (i.e., CIF, 4CIF, PAL and 720p) encoded with four common codecs (i.e., MPEG2, MPEG4, H264 and H265) at different qualities (i.e., different constant and variable bitrates, as well as constant quantization parameters).

This work investigates computational musicology for the study of tape music works tackling the problems concerning stemmatics. These philological problems have been analyzed with an innovative approach considering the peculiarities of audio tape recordings. The paper presents a phylogenetic reconstruction strategy that relies on digitizing the analyzed tapes and then converting each audio track into a two-dimensional spectrogram. This conversion allows adopting a set of computer vision tools to align and equalize different tracks in order to infer the most likely transformation that converts one track into another. In the presented approach, the main editing techniques, intentional and unintentional alterations and different configurations of a tape recorded are estimated in phylogeny analysis. The proposed solution presents a satisfying robustness to the adoption of the wrong reading setup together with a good reconstruction accuracy of the phylogenetic tree. The reconstructed dependencies proved to be correct or plausible in 90% of the experimental cases.