Source video camera identification for multiply compressed videos originating from YouTube

Houten, Wiger van; Geradts, Zeno

doi:10.1016/j.diin.2009.05.003

Cited by 52 publications

(32 citation statements)

References 21 publications

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“…Furthermore, we showed how this dataset allows the exploration of new forensic opportunities such as comparing camera reference fingerprints estimated from still images and from videos. The whole dataset is made available 6 to the research community, along with a guide that clarifies its structure and several csv files containing technical information.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, another limit in the state-of-the-art is represented by the lack of a collection of controlled content coming from social media platforms, like Facebook, YouTube, and WhatsApp; indeed, recent multimedia forensic applications would take advantage in having a large dataset containing such kind of contents: for instance, in [6], the authors address the performance of identifying the source of YouTube videos but limiting to a scenario with videos belonging to 8 webcams of the same model (Logitech Quickcam STX). Similarly, Bertini et al [18] propose to extract the Sensor Pattern Noise from images to identify fake social media accounts, but the technique was tested on 5 mobile devices only, with 200 images each.…”

Section: Motivationmentioning

confidence: 99%

“…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. Indeed, in most papers related to SPN [6][7][8][9], digitally stabilized videos are ignored, either by turning the stabilization off or considering non-stabilized devices only. This is unrealistic, considering that most common modern devices (e.g., Apple iPhones) are equipped with an in-camera digital stabilization system that cannot be turned off without resorting to third party applications.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

VISION: a video and image dataset for source identification

Shullani

Fontani

Iuliani

et al. 2017

EURASIP J. on Info. Security

221

175

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

VISION: a video and image dataset for source identification

Shullani

Fontani

Iuliani

et al. 2017

EURASIP J. on Info. Security

221

175

View full text Add to dashboard Cite

show abstract

“…Houten et al [14] and Hyun et al [15] extended Chen's method to implement on low resolution videos. It is based on enhanced PRNU pattern noise method.…”

Section: Literature Reviewmentioning

confidence: 99%

Source Identification of Videos Transmitted in Lossy Wireless Networks

Kaur¹,

Randhawa²

2017

International Journal of Innovative Research in Electrical, Ele

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Abstract:The sensor pattern noise based method exploits the inherent fingerprint of the camera sensor and is universally applicable. This method has the best performance reported so far among the existing methods and only method effective in lossy wireless network incorporating wireless channel characteristics and protection from security threats like blurring, blocking and spoofing attack. Experiment test was conducted with 20 test each on 8 videos of eight wireless cameras taking randomly 5 frames of the video to be tested using Sensor Pattern Noise based on correlation based coeffient matching and our proposed work based on Gaussian Mixture Model matching. It leads us to conclusion that it is more reliable, accurate and less Complex. It is applicable on different size video, requirement of random frames is very less as compared to existed work and help to create trained data set only once.

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“…As such, statistical techniques that focus on double-compression artifacts [1]- [3], interlaced and de-interlaced correlations [4], or sensor noise patterns [5]- [7] are unlikely to apply. In addition, forensic techniques based on detecting frame or region duplication [8], [9] will only apply when some form of duplication was necessary to create the fake.…”

Section: Introductionmentioning

confidence: 99%

Exposing Digital Forgeries in Ballistic Motion

Conotter

O’Brien

Farid

2012

IEEE Trans.Inform.Forensic Secur.

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Abstract-We describe a geometric technique to detect physically implausible trajectories of objects in video sequences. This technique explicitly models the three-dimensional ballistic motion of objects in free-flight and the two-dimensional projection of the trajectory into the image plane of a static or moving camera. Deviations from this model provide evidence of manipulation. The technique assumes that the object's trajectory is substantially influenced only by gravity, that the image of the object's center of mass can be determined from the images, and requires that any camera motion can be estimated from background elements. The computational requirements of the algorithm are modest, and any detected inconsistencies can be illustrated in an intuitive, geometric fashion. We demonstrate the efficacy of this analysis on videos of our own creation and on videos obtained from videosharing web-sites.

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Source video camera identification for multiply compressed videos originating from YouTube

Cited by 52 publications

References 21 publications

VISION: a video and image dataset for source identification

VISION: a video and image dataset for source identification

Source Identification of Videos Transmitted in Lossy Wireless Networks

Exposing Digital Forgeries in Ballistic Motion

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