Indeed, in most papers related to SPN, digitally stabilized videos are ignored, either by turning the stabilization off or considering non-stabilized devices only.

In particular, the proposed dataset contains several devices featuring in-camera digital stabilization, that is known to threaten source identification based on sensor pattern noise. To exemplify the usefulness of the VISION dataset, we test the performance of a well-known forensic tool, i.e., the detection of the sensor pattern noise (SPN) left by the acquisition device for the source identification of native/social media contents moreover, we describe some new opportunities deriving by the availability of images and videos captured with the same sensor to find a solution to current limits present in the literature. Furthermore, we acquired 648 native videos, 622 of which were shared through YouTube at the maximum available resolution, and 644 through WhatsApp, resulting in a total of 1914 videos 1. Overall, we collected 11,732 native images 7565 of them were shared through Facebook, in both high and low quality, and through WhatsApp, resulting in a total of 34,427 images. In this paper, we present a new dataset of native images and videos captured with 35 modern smartphones/tablets belonging to 11 different brands: Apple, Asus, Huawei, Lenovo, LG electronics, Microsoft, OnePlus, Samsung, Sony, Wiko, and Xiaomi. This motivates the need for a new dataset containing a heterogeneous and sufficiently large set of visual data-both images and videos-as benchmark to test and compare forensic tools. These devices are also capable to acquire both videos and images exploiting the same sensor, thus opening new investigation opportunities in linking different kind of contents. Indeed, most multimedia contents are currently acquired by portable devices that keep updating year by year. Unfortunately, these datasets, especially for the case of videos, are outdated and non-representative of real case scenarios. Generally, the effectiveness of a forensic technique should be verified on image and video datasets that are freely available and shared among the community.

During the years, the research community developed several tools to analyze a digital image, focusing on issues related to the identification of the source device and the assessment of content authenticity. In this scenario, multimedia forensics (MF) has been proposed as a solution for investigating images and videos to determine information about their life cycle. Such information may become relevant when visual data are involved in a crime. Given their digital nature, these data also convey several information related to their life cycle (e.g., source device, processing they have been subjected to). Furthermore, one third of the people can go online and upload their pictures on websites and social networks. Statistics show that a relevant portion of the world’s population owns a digital camera and can capture pictures. The wild world of web also allows users to easily share visual contents through social media platforms. Images and videos are used to convey persuasive messages to be used under several different environments, from propaganda to child pornography.

In the last decades, visual data gained a key role in providing information. VISION can be exploited as benchmark for the exhaustive evaluation of several image and video forensic tools. The VISION dataset is currently composed by 34,427 images and 1914 videos, both in the native format and in their social version (Facebook, YouTube, and WhatsApp are considered), from 35 portable devices of 11 major brands. The goal of this paper is to propose the VISION dataset as a contribution to the development of multimedia forensics. On the other hand, portable devices capture both images and videos with the same sensor, opening new forensic opportunities. These facts pose new challenges to the forensic community: for example, most modern cameras feature digital stabilization, that is proved to severely hinder the performance of video source identification technologies moreover, the strong re-compression enforced by SMPs during upload threatens the reliability of multimedia forensic tools. Remarkably, in the latest years, portable devices became the preferred means to capture images and videos, and contents are commonly shared through social media platforms (SMPs, for example, Facebook, YouTube, etc.). However, the performance of proposed tools are usually tested on data that are far from reality in terms of resolution, source device, and processing history. Forensic research community keeps proposing new techniques to analyze digital images and videos.

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