2021 Prnu

2021 Prnu

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January 2021 Journal of Imaging 7(1):8
This person is not on ResearchGate, or hasn't claimed this research yet.
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.
p) Figure 2 (1080p) Highest correlation value between 0.13 and 0.23 between 0.27 and 0.67 Average correlation value between 0.08 and 0.15 between 0.25 and 0.58 Lowest correlation value between 0.06 and 0.12 between 0.22 and 0.44
23 comparisons, done with images made with an iPhone 6, all with a resolution of 720p. In Figure 1 all 23 comparisons have a comparable lowest correlation value and the average correlation value varies a little. The highest correlation values per comparison vary more. In Figure 1 comparison 1 has a little spread, compared to the other comparisons. In Figure 2 the first seven comparisons are very close together, the rest of the 23 comparisons are more scattered. The highest and lowest value are much further apart. Comparison 10 is noticeable; it only has a highest correlation value of 0.27.
Overview of the amount of videos per factor examined.
Overview of the highest, average and lowest correlation values for both resolutions compared to Snapchat.
Results of the mutual comparisons of both videos with different lengths and videos with the same length (10 s).
Content may be subject to copyright.
Content may be subject to copyright.
Factors that Inﬂuence PRNU-Based Camera-Identiﬁcation
Lars de Roos 1, 2, * and Zeno Geradts 2,3
Camera-Identiﬁcation via Videos. J.
Imaging 2021 , 7 , 8. https://doi.org/
tral with regard to jurisdictional clai-
ms in published maps and institutio-
Copyright: © 2021 by the authors. Li-
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (https://
1 Faculty of Technology , Amster dam University of Applied Sciences, 1097 DZ Amsterdam, The Netherlands
2 Department of Digital and Biometric Traces, Netherlands For ensic Institute, 2467 GB The Hague,
3 Faculty of Science, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
* Correspondence: larssamderoos@gmail.com
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 wer e
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 (refer ence) 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
PRNU; photo response non-uniformity; source camera identiﬁcation; videos; compres-
Each cam era cre ates a high ly chara cterist ic patte rn: The Photo Resp onse Non- Unifor mity
patter n (PRNU -patte rn). The PRNU-pattern is caused by differences in material pr operties
and due to proximity effects during the production process of the image sensor. This
pattern can be compared with various software in order to answer the following questions:
‘which camera is the source of a speciﬁc photo or video’ and ‘are certain photos or videos
taken with the same camera’. After this comparison, a correlation value is linked to
it, which describes the degree of similarity . In some cases, inexplicable low correlation
values were measured when comparing videos. Several initiatives have alr eady been
taken by the Netherlands Forensic Institute (NFI) to determine the causes of these low
correlation values. This was done by conducting small studies and proﬁciency tests
in which international organizations participated. Since the size of these studies was
limited, in most cases this matter has not been published. This study therefore made an
overview of the factors already investigated. Based on this list of more than 50 different
factors, three factors were chosen that could contribute to the broadening of knowledge
regarding the factors that inﬂuence the PRNU-pattern. These factors include the following:
compression, resolution and the length of the video. It is expected that these factors
will negatively inﬂuence the PRNU-pattern, resulting in a low correlation value when
a comparison is made. In previous studies [
] it was found that compression had a
J. Imaging 2021 , 7 , 8. https://doi.org/10.3390/jimaging7010008 https://www.mdpi.com/journal/jimaging
negative inﬂuence on the PRNU-pattern. Since Snapchats compression had not yet been
investigated, this factor was chosen. Currently not much is possible in terms of getting
information from the Snapchat application. A method to determine whether an image
comes from the Snapchat application of a phone is therefore a welcome addition. These
“Snapchat image comparisons” can also be very important to increase the burden of proof
when normal reference images are missing or when large quantities of social media images
have to be compared with each other . In this report the investigation regarding Snapchat
serves as a starting point for further investigations. In addition to this partial study , the
inﬂuence of resolution on the PRNU-pattern is being investigated. Some research has
been conducted into video resolutions higher than 720p, but not enough to draw more
]. This partial study attempts to contribute to the formation of these
more general conclusions. The last factor, the inﬂuence of the length of the video, was
chosen on the basis of a recommendation that was given in a study into the inﬂuence of
movement and stabilization of drones on the PRNU-pattern [
described that this factor may contribute to the deterioration of the PRNU-pattern. This
paper therefore looks at three differ ent factors that can inﬂuence the PRNU-pattern and
with that the correlation value that comes from the comparison of these PRNU-patterns.
The factors that have already been investigated by the NFI are also included. The aim of
this research is therefore to determine which factors may pr ovide low correlation values
when comparing videos. It evaluates the computation and comparison methods used,
under variation of these certain factors.
Now that an introduction has been given, the rest of this paper consists of the fol-
lowing: The chapter state of the art describes the basics of PRNU-investigation. The
materials and methods chapter gives information regarding the choices made. The results
are presented and later discussed in the chapters results and discussion. Subsequently,
a conclusion has been formulated. All chapters are written by Lars de Roos, under the
Photo Response Non-Uniformity is a way in which errors in the output of the image
]. PRNU describes the difference between the actual response of
the image sensor and a uniform response [
]. During the production process PRNU occurs
due to the impurity of the raw material or by the variation in size of the photodiode due
to proximity effects. Since PRNU is caused by these physical properties, the characteristic
]. Furthermore, the amount of noise depends on the
light: if there is a lot of light, or if settings are used that let much light enter the camera, this
will lead to a lot of noise. The differences and variations that arise create a noise pattern
(also called a PRNU-pattern). This pattern is present in every photograph that the image
sensor produces. The pattern is often seen as the “ﬁngerprint” of the image sensor, and
]. The production of the ﬁngerprint of the camera has grown
over the years to be the golden standard when comparing digital images.
The PRNU-pattern can be made visible with advanced software, such as PRNUCom-
]. With this software the source of an image can be retrieved. This is done with the
same steps as described by Meij and Geradts [
]. In this study, steps 4 and 5 whereby the
zero mean and Wiener ﬁlter ar e used to remove noise and artifacts created due to compres-
sion, were skipped in order to investigate the inﬂuence of compression. Reference cameras
are needed to make reference images, also called ﬂatﬁeld images. These are images of a
gray surface where the light is distributed as evenly as possible over the pixels of the image
sensor. The PRNU-patterns that come from the reference images can then be compared
to the images whose source has to be retrieved. In the software, such a comparison can
be performed. A correlation value is calculated for this comparison which describes the
degree of similarity between the PRNU-patterns.
Multiple studies have been conducted over the past few years regarding the analysis of
camera images. In the early stages these were mainly focused on the possibilities that Fixed
Pattern Noise (FPN)—which includes Photo Response Non-Uniformities—had to offer [
Furthermore, it was also discovered that it was possible to identify a camera on the basis of
]. Ultimately, the method of Photo Response Non-Uniformities was further
developed. For example, more complex ﬁlters and algorithms were introduced [
Due to further developments within this subject, even images of poorer quality could be
]. The goals were also adjusted. In addition to identifying the camera, it
became possible to identify fake images [
]. Even before the turn of the century it was
possible to identify a video camera on the basis of videos. However, this did not concern
current digital videos but video tapes [
]. The identiﬁcation of current digital videos
started around 2007, when it was found possible to identify a c
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