Identification of smartphone brand and model via forensic video analysis

Villalba, Luis Javier García; Orozco, Ana Lucila Sandoval; López, Raquel Ramos; Castro, Julio Hernandez

doi:10.1016/j.eswa.2016.01.025

Cited by 21 publications

(5 citation statements)

References 9 publications

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“…Villalba et al [17] presented a method for video source acquisition identification based on SPN extraction from video key frames. Photo response non-uniformity (PRNU) is the primary part of the SPN in an image.…”

Section: Related Workmentioning

confidence: 99%

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Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Yao

Zhang

et al. 2018

Sensors

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Computer-generated graphics (CGs) are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.

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Section: Related Workmentioning

confidence: 99%

“…Photo response non-uniformity (PRNU) is the primary part of the SPN in an image. In [17], the PRNU is used to calculate the SPN and characterize the fingerprints into feature vectors. The feature vectors are then extracted from the video key frames and trained by an SVM-based classifier.…”

Section: Related Workmentioning

confidence: 99%

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Yao

Zhang

et al. 2018

Sensors

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“…The methods presented based on machine learning are summarized in Table 2. Two studies López et al (2021) and Villalba et al (2016) mentioned in general categories can also be considered machine learning methods. Su et al (2010) identified source cameras based on the features extracted from bit stream, quantification factor and motion vectors and classified them into camera classes by an SVM classifier.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Digital forensic analysis for source video identification: A survey

Akbari

Al‐Maadeed

Elharrouss

et al. 2022

Forensic Science International: Digital Investigation

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“…To keep up with this, several studies have been conducted in recent years concerning smartphones, WhatsApp, YouTube and drones [2,4,25,26]. For example, it is now possible to identify the brand and model of a smartphone via video analysis [27]. In most literature that has been discussed so far, there is no explicit mention in the results, or in the interpretation and discussion of those results, that there were problems with, for instance, factors influencing the (PRNU-)patterns.…”

Section: Related Workmentioning

confidence: 99%

Factors that Influence PRNU-Based Camera-Identification via Videos

Roos

Geradts

2021

J. Imaging

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The Photo Response Non-Uniformity pattern (PRNU-pattern) can be used to identify the source of images or to indicate whether images have been made with the same camera. This pattern is also recognized as the “fingerprint” of a camera since it is a highly characteristic feature. However, this pattern, identically to a real fingerprint, is sensitive to many different influences, e.g., the influence of camera settings. In this study, several previously investigated factors were noted, after which three were selected for further investigation. The computation and comparison methods are evaluated under variation of the following factors: resolution, length of the video and compression. For all three studies, images were taken with a single iPhone 6. It was found that a higher resolution ensures a more reliable comparison, and that the length of a (reference) video should always be as high as possible to gain a better PRNU-pattern. It also became clear that compression (i.e., in this study the compression that Snapchat uses) has a negative effect on the correlation value. Therefore, it was found that many different factors play a part when comparing videos. Due to the large amount of controllable and non-controllable factors that influence the PRNU-pattern, it is of great importance that further research is carried out to gain clarity on the individual influences that factors exert.

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Identification of smartphone brand and model via forensic video analysis

Cited by 21 publications

References 9 publications

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Digital forensic analysis for source video identification: A survey

Factors that Influence PRNU-Based Camera-Identification via Videos

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