Digital Video Source Identification Based on Container’s Structure Analysis

Abstract

The mobile device ecosystem has dramatically evolved over the last few years, since users have openly embraced a massive use of mobile phones for different purposes: professional use, personal use, etc. Digital videos can be used to define legal responsibilities or as part of the evidence in trials. The forensic analysis of digital videos becomes very relevant to determine the origin and authenticity of a video in order to link an individual with a device, place or event. The field of forensic analysis of digital videos is constantly facing new and direct challenges. Even though the basic principles of this discipline remain unchanged, numerous issues appear every year that require new procedures and tools. Therefore, it is necessary to provide forensic analysts with techniques to identify the origin of multimedia content. In this paper, the topic of source identification in open scenarios will be discussed, since analysts do not know in advance the set of cameras to which a video belongs so they find it difficult to identify its source. This approach is similar to real-life situations since in most cases, analysts are unaware of the set of video cameras. This paper aims to create a technique that identifies the source of digital videos generated by digital devices through the use of unsupervised algorithms based on the analysis of the structure of multimedia video devices. INDEX TERMS Acquisition source identification, clustering analysis, container atoms, forensics analysis, video container.

Identification of Social-Media Platform of Videos through the Use of Shared Features

“…Another work [30] from the same research group combines the PRNU and Noiseprint to boost the performance of PRNUbased analyses based on only a few images. In some works [8,31,32] video file containers have been considered for the source identification of videos without a prior training phase. To do this, López et al [32] introduces a hierarchical clustering method whereas [8] proposes a likelihood-ratio framework.…”

“…In some works [8,31,32] video file containers have been considered for the source identification of videos without a prior training phase. To do this, López et al [32] introduces a hierarchical clustering method whereas [8] proposes a likelihood-ratio framework. Mayer et al [33] propose a similarity network based on [1] to extract features from video patches, and to fuse multiple comparisons to produce a video-level verification decision.…”

Untitled

“…David Gijeera et al [7] in the work "We Need No Pixels: Video Manipulation Detection Using Stream Descriptors" mentioned that propose to identify forged videos by analyzing their multimedia stream descriptors with simple binary classifiers. RAQUEL RAMOS LÓPEZ et al [8] in the work "We Need No Pixels: Video Manipulation Detection Using Stream Descriptors" mentioned that propose to identify forged videos by analyzing their multimedia stream descriptors with simple binary classifiers. BRIAN C. HOSLER et al [9] Created the data bank of file's container structure of different camera and mobile phone and the same were utilized for source identification.…”

Deep Learning-based Cnn Multi-modal Camera Model Identification for Video Source Identification

“…It is important to note that, for visual content, we use patches cropped from the frames and, for audio content, we use patches cropped from the Log-Mel Spectrogram (LMS) of the audio track in the video that is used to solve the identification problem light of this, the method suggested by falls into the mono-modal category, since the authors rely solely on the visual content of a query video in order to determine its classification. In order to identify multimodal camera models, we propose two distinct approaches based on this information [25]. With both approaches, we make use of CNNs and feed them with a pair of visual and audio patches in order to feed them with information.…”