Development of a video tampering dataset for forensic investigation

Al-Sanjary, Omar Ismael; Ahmed, Ahmed A.; Sulong, Ghazali

doi:10.1016/j.forsciint.2016.07.013

Cited by 55 publications

(29 citation statements)

References 12 publications

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“…The performance of proposed technique was measured on different datasets taken from Hsu et al [25], Bestaigini et al [16], Ariddizone and Mazzola [8], and Sanjary et al [6] which are summarized in Table I. Sample frames taken from authentic and forged video sequences of these datasets are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Robust Video Content Authentication using Video Binary Pattern and Extreme Learning Machine

Sadddique¹,

Asghar²,

Mehmood³

et al. 2019

IJACSA

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Recently, due to easy accessibility of smartphones, digital cameras and other video recording devices, a radical enhancement has been experienced in the field of digital video technology. Digital videos have become very vital in court of law and media (print, electronic and social). On the other hand, a widely-spread availability of Video Editing Tools (VETs) have made video tampering very easy. Detection of this tampering is very important, because it may affect the understanding and interpretation of video contents. Existing techniques used for detection of forgery in video contents can be broadly categorized into active and passive. In this research a passive technique for video tampering detection in spatial domain is proposed. The technique comprises of two phases: 1) Extraction of features with proposed Video Binary Pattern (VBP) descriptor, and 2) Extreme Learning Machine (ELM) based classification. Experimental results on different datasets reveal that the proposed technique achieved accuracy 98.47%.

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Section: Resultsmentioning

confidence: 99%

Robust Video Content Authentication using Video Binary Pattern and Extreme Learning Machine

Sadddique¹,

Asghar²,

Mehmood³

et al. 2019

IJACSA

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“…The SULFA dataset was also extended by the REWIND dataset [16]; anyway, these datasets are less interesting for video source identification, since they contain few digital cameras only and no smartphone, while we know smartphones are the most representative kind of device today, especially for applications on social media platforms. Recently, the video tampering dataset (VTD) was provided by Al-Sanjary et al [17]. The VTD, focused on video tampering detection on videos collected from the YouTube platform, is composed by 33 downloaded videos, 16-s long, at 30 fps with a HD resolution.…”

Section: Motivationmentioning

confidence: 99%

VISION: a video and image dataset for source identification

Shullani

Fontani

Iuliani

et al. 2017

EURASIP J. on Info. Security

217

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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“…In [34] the authors supply tampered video along with an explicit pixel level binary mask detailing the chroma-keyed addition. Some video tampering datasets come complete with original and tampered videos, thus providing a means to calcualte all masks and labels associated with tampering [41,98]. This allows for tampering detection and localisation in spatial and temporal domains.…”

Section: Tampered Video Datasetsmentioning

confidence: 99%

“…Using differences between original and tampered videos may be inappropriate for temporally tampered videos [98], where a frame-by-frame label might provide more information. This can be achieved when unprocessed original and tampered sequences are provided.…”

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confidence: 99%

A review of digital video tampering: From simple editing to full synthesis

Johnston

Elyan

2019

Digital Investigation

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Video tampering methods have witnessed considerable progress in recent years. This is partly due to the rapid development of advanced deep learning methods, and also due to the large volume of video footage that is now in the public domain. Historically, convincing video tampering has been too labour intensive to achieve on a large scale. However, recent developments in deep learning-based methods have made it possible not only to produce convincing forged video but also to fully synthesize video content. Such advancements provide new means to improve visual content itself, but at the same time, they raise new challenges for state-of-the-art tampering detection methods. Video tampering detection has been an active field of research for some time, with periodic reviews of the subject. However, little attention has been paid to video tampering techniques themselves. This paper provides an objective and in-depth examination of current techniques related to digital video manipulation. We thoroughly examine their development, and show how current evaluation techniques provide opportunities for the advancement of video tampering detection. A critical and extensive review of photo-realistic video synthesis is provided with emphasis on deep learning-based methods. Existing tampered video datasets are also qualitatively reviewed and critically discussed. Finally, conclusions are drawn upon an exhaustive and thorough review of tampering methods with

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Development of a video tampering dataset for forensic investigation

Cited by 55 publications

References 12 publications

Robust Video Content Authentication using Video Binary Pattern and Extreme Learning Machine

Robust Video Content Authentication using Video Binary Pattern and Extreme Learning Machine

VISION: a video and image dataset for source identification

A review of digital video tampering: From simple editing to full synthesis

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