Using Sensor Noise to Identify Low Resolution Compressed Videos from YouTube

Houten, Wiger van; Geradts, Zeno

doi:10.1007/978-3-642-03521-0_10

Cited by 10 publications

(7 citation statements)

References 28 publications

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“…We next verify the theoretical analysis, and thus validate the simplifying assumptions made, experimentally on synthetic signals. Figure 1 confirms a close match between σ 2 ρ (0, 0) (19) and its value obtained from 10,000 Monte Carlo simulations as a function of the JPEG quality factor when the variance of the two compared PR-NUs is σ 2 R = σ 2 S = 10 and σ 2 R = σ 2 S = 100, for image size n 1 × n 2 = 1000 × 1000. Furthermore, Table 1 compares the variance of the normalized correlation σ 2 ρ (τ 1, τ 2 ) as a function of the considered shift for the theoretical expression in (19) with the variance estimated using 5,000 Monte Carlo realizations.…”

Section: Normalized Correlationsupporting

confidence: 76%

“…The methodology is applicable to all digital imaging devices that contain CCD or CMOS sensors. The vast majority of published work focuses on still images with only a handful of papers directed to digital videocameras [3,8,18,19,13]. Lossy compression that is typically applied to images and video complicates setting ap-propriate detection thresholds to guarantee a preset false alarm (mistakenly identifying an image as coming from a specific camera).…”

Section: Motivationmentioning

confidence: 99%

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Effect of Compression on Sensor-Fingerprint Based Camera Identification

Goljan¹,

Chen²,

Comesaña³

et al. 2016

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Camera identification using sensor fingerprints is nowadays a well-established technology for reliably linking an image or a video clip to a specific camera. The sensor fingerprint is typically estimated from images (video frames) provably taken by the imaging device. An image or a video clip is then associated with the fingerprint when some form of a matched filter exceeds a certain threshold, which is set to achieve a prescribed false alarm. However, when the images from which the sensor fingerprint is estimated are lossy compressed, the statistical properties of the detection statistic change, which requires an adjustment of the decision threshold to guarantee the same false-alarm rate. In this paper, we study this effect both theoretically and experimentally. A very good match between the theoretical and experimental results validates our approach. This study is especially important for video forensic because of the higher compression rates.

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Section: Normalized Correlationsupporting

confidence: 76%

Section: Motivationmentioning

confidence: 99%

Effect of Compression on Sensor-Fingerprint Based Camera Identification

Goljan¹,

Chen²,

Comesaña³

et al. 2016

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“…This representation is also known as biometric template. The method we adopted is based on the histogram of oriented gradients (HOG) [12]. The idea behind this technique is that object appearance and shape can be represented by the distribution of local intensity gradients (i.e.…”

Section: Face Recognitionmentioning

confidence: 99%

Untitled

2018

IJET

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Pew Research Centre). In 2016, Kaspersky Security Network (KSN), estimated mobile banking attacks increase of 1.6 times, compared to 2015. Pew Research Centre also reports (January 2016) that 28% of smartphone owners do not use a screen lock or other security features in order to access their phone or protect sensitive data stored on it. Finally, Acuity Market Intelligence has published its latest "Biometric Smartphone Update", which reveals that the number of smartphones incorporating biometrics has doubled since January 2016. These data define the scenario that has given rise to the proposed authentication protocol. On one hand, there is the ever-increasing need of secure

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“…The challenging problem of video source identification from low-quality videos has been deeply explored by van Houten et al [40][41][42] in several works. The authors recorded videos using several different cameras, with various resolutions and bitrates.…”

Section: Prnu Based Source Identificationmentioning

confidence: 99%

An overview on video forensics

Bestagini

Fontani

Milani

et al. 2012

SIP

196

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The broad availability of tools for the acquisition and processing of multimedia signals has recently led to the concern that images and videos cannot be considered a trustworthy evidence, since they can be altered rather easily. This possibility raises the need to verify whether a multimedia content, which can be downloaded from the internet, acquired by a video surveillance system, or received by a digital TV broadcaster, is original or not. To cope with these issues, signal processing experts have been investigating effective video forensic strategies aimed at reconstructing the processing history of the video data under investigation and validating their origins. The key assumption of these techniques is that most alterations are not reversible and leave in the reconstructed signal some "footprints", which can be analyzed in order to identify the previous processing steps. This paper presents an overview of the video forensic techniques that have been proposed in the literature, focusing on the acquisition, compression, and editing operations, trying to highlight strengths and weaknesses of each solution. It also provides a review of simple processing chains that combine different operations. Anti-forensic techniques are also considered to outline the current limitations and highlight the open research issues

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Using Sensor Noise to Identify Low Resolution Compressed Videos from YouTube

Cited by 10 publications

References 28 publications

Effect of Compression on Sensor-Fingerprint Based Camera Identification

Effect of Compression on Sensor-Fingerprint Based Camera Identification

Untitled

An overview on video forensics

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