Forensic research community keeps proposing new techniques to analyze digital images and videos. 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. 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.). 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. On the other hand, portable devices capture both images and videos with the same sensor, opening new forensic opportunities. The goal of this paper is to propose the VISION dataset as a contribution to the development of multimedia forensics. 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. VISION can be exploited as benchmark for the exhaustive evaluation of several image and video forensic tools.

中文翻译：

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视觉：用于源识别的视频和图像数据集

法医研究社区不断提出新技术来分析数字图像和视频。但是，通常会在分辨率，源设备和处理历史方面就远非现实的数据测试所提出工具的性能。值得注意的是，在最近几年中，便携式设备成为捕获图像和视频的首选方法，并且内容通常通过社交媒体平台（例如，SMP，Facebook，YouTube等）共享。这些事实对法医界提出了新的挑战：例如，大多数现代相机具有数字稳定功能，事实证明，这些功能严重阻碍了视频源识别技术的性能；此外，SMP在上传过程中强加的重新压缩会威胁多媒体取证工具的可靠性。另一方面，便携式设备使用同一传感器捕获图像和视频，从而开辟了新的法医机会。本文的目的是提出VISION数据集，以促进多媒体取证的发展。VISION数据集目前由来自11个主要品牌的35台便携式设备的34,427张图像和1914个视频组成，包括本机格式和社交版本（考虑使用Facebook，YouTube和WhatsApp）。可以将VISION用作对几种图像和视频取证工具进行详尽评估的基准。来自11个主要品牌的35种便携式设备的原始格式和社交版本（考虑使用Facebook，YouTube和WhatsApp）。可以将VISION用作对几种图像和视频取证工具进行详尽评估的基准。来自11个主要品牌的35种便携式设备的原始格式和社交版本（考虑使用Facebook，YouTube和WhatsApp）。可以将VISION用作对几种图像和视频取证工具进行详尽评估的基准。