On the Usage of Sensor Pattern Noise for Picture-to-Identity Linking

through Social Network Accounts

Riccardo Satta

and Pasquale Stirparo

1,2

Institute for the Protection and Security of the Citizen

Joint Research Centre (JRC), European Commission, Ispra (VA), Italy

Royal Institute of Technology (KTH), Stockholm, Sweden

Keywords:

Social Network, Account, Sensor Pattern Noise, Identity, Linking, Digital Image Forensics, Multimedia

Forensics.

Abstract:

Digital imaging devices have gained an important role in everyone’s life, due to a continuously decreasing

price, and of the growing interest on photo sharing through social networks. As a result of the above facts,

everyone continuously leaves visual “traces” of his/her presence and life on the Internet, that can constitute

precious data for forensic investigators. Digital Image Forensics is the task of analysing such digital images for

collecting evidences. In this ﬁeld, the recent introduction of techniques able to extract a unique “ﬁngerprint”

of the source camera of a picture, e.g. based on the Sensor Pattern Noise (SPN), has set the way for a series of

useful tools for the forensic investigator. In this paper, we propose a novel usage of SPN, to ﬁnd social network

accounts belonging to a certain person of interest, who has shot a given photo. This task, that we name

Picture-to-Identity linking, can be useful in a variety of forensic cases, e.g., ﬁnding stolen camera devices,

cyber-bullying, or on-line child abuse. We experimentally test a method for Picture-to-Identity linking on

a benchmark data set of publicly accessible social network accounts collected from the Internet. We report

promising result, which show that such technique has a practical value for forensic practitioners.

1 INTRODUCTION

Nowadays, digital imaging devices have gained a

prominent role in everyone’s life. Mobile smart

phones, tablets, digital cameras and camcorders have

become progressively cheaper and affordable for ev-

ery one; this goes hand in hand with the growing in-

terest for sharing moments of our life using social net-

works (e.g., Facebook, Flickr) and Internet in general.

As a result, everyone of us is continuously leaving vi-

sual “traces” of his/her presence and life on the Inter-

net. Under the proper legal framework, law enforcers

and forensic investigator can access this data (e.g., in

undercover operations) in case it is relevant for inves-

tigations.

The task of analysing digital images for forensic

purposes is usually referred to as digital image foren-

sics. In this ﬁeld, the recent introduction of tech-

niques able to extract a unique “ﬁngerprint” of the

source camera of a picture (Dirik et al., 2008; Li,

2010; Lukas et al., 2006) has set the way for a se-

ries of useful tools for the forensic investigator. In

particular, the Sensor Pattern Noise (SPN) left in the

image by the device sensor has been exploited in var-

ious forensic tasks like source device identiﬁcation

(Lukas et al., 2006), forgery detection (Li and Li,

2012), source device linking (Fridrich, 2009), or clus-

tering of images with respect of the source camera (Li

and Li, 2012).

In this paper, we present a novel usage of the SPN

for digital image forensic purposes. We propose to ex-

ploit SPN ﬁngerprints to ﬁnd social network accounts

belonging to a certain person of interest, who has shot

a given, known photo. We name this task Picture-

to-Identity linking. It can be useful in a variety of

forensic cases, e.g., on-line child abuse, defamation,

ﬁnding stolen camera devices. To the authors’ best

knowledge, this application of the SPN has never been

proposed in the literature so far. We developed an im-

plementation of Picture-to-Identity linking based on

the SPN extraction method proposed by (Lukas et al.,

2006), and tested it on a benchmark data set of social

network accounts collected from the Internet. The re-

ported results are promising and show evidence of a

practical usefulness of such technique for forensic in-

vestigators.

Satta R. and Stirparo P..

On the Usage of Sensor Pattern Noise for Picture-to-Identity Linking through Social Network Accounts.

DOI: 10.5220/0004682200050011

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 5-11

ISBN: 978-989-758-009-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Image acquisition pipeline in typical camera devices.

The remainder of the paper is structured as fol-

lows. First, in Sect. 2 we review previous works on

source camera identiﬁcation, with particular regard

to SPN. We then focus in greater detail on the us-

age of SPN for Picture-to-Identity linking in Sect. 3,

providing an overview of the possible concrete appli-

cations and of the challenges that must be expected.

Sect.4 describes and formalises a method for Picture-

to-Identity linking, that is then experimentally tested

on a benchmark data set in Sect. 5. Finally, Sect. 6

draws up conclusions and suggests possible directions

for further research on this topic.

2 RELATED WORK

Digital images can be associated to various kinds of

useful metadata. Examples are Exif data, image tags,

or text associated to the image (e.g., contained in

the same web page), etc. Exif metadata in partic-

ular has received much attention by forensic inves-

tigators, since it stores useful information about the

device (e.g., camera model, serial number, etc.) that

produced the content. However, from a forensic point

of view, this information has to be taken into account

with extreme care, as it is fairly easy to modify or re-

move it with image processing software (e.g., Photo-

shop) or with free tools available on the Internet (e.g.,

ExifTool). A robust cue that can be used in place of

Exif data to identify the source camera of a picture

is the noise pattern left by the sensor element of the

camera (usually referred to as Sensor Pattern Noise or

SPN) (Lukas et al., 2006). In fact, such noise pattern

is univocal of a camera sensor, and can be seen as an

unique ﬁngerprint that identiﬁes an individual device.

To proper understand how the SPN can be used

as a ﬁngerprint, it is worth to take a closer look at

how a digital picture is typically produced by a cam-

era (Li, 2010) (see a clarifying scheme in Fig. 1). The

light coming from the scene arrives ﬁrst to the cam-

era lens. Then, it passes through an anti-aliasing ﬁl-

ter, and reaches the Colour Filter Array (CFA), which

is placed just over the sensor and is used to cap-

ture colour information. The light ﬁnally reaches

the sensor, a matrix of elementary sensitive elements

each corresponding to a pixel, that converts light into

a digital representation. The subsequent steps, de-

mosaicking and post-processing, are respectively in

charge of interpolating the missing two colours of

each pixel, and of carrying out image processing op-

erations (e.g., white balancing, de-noising, etc.) to in-

crease the perceived image quality. Each step of this

pipeline may leave artefacts on the image that can be

used as a signature of the camera device.

Much research has been conducted in this direc-

tion, exploiting SPN (Kang et al., 2012; Li, 2010; Li

and Li, 2012; Lukas et al., 2006), interpolation arte-

facts caused by de-mosaicking ﬁlter (Cao and Kot,

2009; Long and Huang, 2006; Popescu and Farid,

2005) and JPEG compression (Sorrell, 2009), traces

of dust in the sensor (Dirik et al., 2008), and lens

aberrations (Choi et al., 2006; Van et al., 2007), as

possible ﬁngerprints. Out of them, de-mosaicking and

JPEG compression artefacts depend on the algorithms

chosen by the manufacturer, which are usually spe-

ciﬁc of the model; therefore, they can be used only

as a signature of the camera model (not of individual

cameras). Dust traces affect mainly professional re-

ﬂex cameras with interchangeable lens (dust may en-

ter inside the camera when the photographer changes

the lens) and are a ﬁngerprint of the single device,

that however exhibits a low stability over time (i.e.

new dust particles may be deposited into the sensor).

Regarding lens aberrations, their use as device ﬁnger-

print has been tested in a limited extent (Choi et al.,

2006; Van et al., 2007) and its actual potential is still

to be explored.

Differently from the above techniques, the Sen-

sor Pattern Noise has the desired characteristics of

uniqueness and stability, and has been studied and

tested in various forensic tasks, e.g.: source device

identiﬁcation (Kang et al., 2012; Lukas et al., 2006;

Li, 2010; Li and Li, 2012; Li and Satta, 2012), forgery

detection (Li and Li, 2012), source device linking

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

(Fridrich, 2009), clustering of images with respect of

the source camera (Li and Li, 2012).

Most techniques extract the SPN by exploiting the

additive noise model ﬁrst presented by (Lukas et al.,

2006), which models SPN as an additive, high fre-

quency noise. In the wavelet domain, this can be for-

mulated as:

= DW T (P) − F



DW T (P)



(1)

where P is a picture, n

is the SPN of P, DW T (·)

is the Discrete Wavelet Transform, and F is a de-

noising ﬁlter, which extracts the low-frequency (non-

noise) components of P. The de-noising ﬁlter F used

plays indeed a crucial role. The wavelet-based ﬁlter

described in Appendix A of (Lukas et al., 2006) is an

effective one and has been used in many other works

(Li, 2010; Li and Li, 2012; Li and Satta, 2012).

In order to compare two SPNs n

and n

from

two images P1 and P2, a common approach (Li, 2010;

Li and Li, 2012; Li and Satta, 2012) is to evaluate the

Normalised Cross-Correlation, which is deﬁned as

ρ(n

) =

− ¯n

) · (n

− ¯n

)

− ¯n

k · kn

− ¯n

(2)

where ¯n

and ¯n

are the means of n

and n

respectively. The value of ρ(n

) can be taken as

the matching score between n

and n

3 PICTURE-TO-IDENTITY

LINKING USING SPN

Most social networks (e.g., Facebook, Google Plus)

offer users the possibility of uploading pictures to en-

rich their proﬁle. Various social networks, like Flickr

or Ipernity, are entirely devoted at sharing photos. In-

deed, from a forensic viewpoint, these personal ac-

counts may offer much useful information for investi-

gations.

Finding social networks accounts that belong to

a certain person of interest (e.g., who is relevant for

a case) can be therefore very valuable. The task is

a non-trivial one, even if one knows identity details.

In fact, one should expect to ﬁnd several homonyms,

and the person of interest may use a pseudonym or

a nick name. Nevertheless, from the perspective of

digital forensics investigations, being able to go back

to the person who shot a picture is important in several

scenarios. Here we mention three of them, varying

from lower to higher level of seriousness.

The ﬁrst one is the case of a stolen smart-phone.

After the theft, the thief will possibly start using the

smart-phone, taking pictures and sharing them on-line

Figure 2: Picture-to-Identity Linking.

on social platforms. Having already pictures taken by

the victim as sample, using the methodology we pro-

pose in this paper it would be possible to correlate

the pictures in order to identify the thief. Another

scenario is the case of defamation torts. Once they

mainly used to happen verbally (e.g. during public

speeches) or in a written form (e.g. releasing inter-

views to the newspaper), nowadays in the Internet era

they are often perpetrated posting on websites/social

networks pictures of which the targeted person may

be ashamed of, maybe because taken without he/she

being aware of it. A clear example of this is cyber-

bullying. Finally, a serious crime which heavily in-

volves digital forensics, is the on-line abuse of chil-

dren, where the perpetrators often record moments of

the ongoing crime by different means, taking video or

photo to later share them over the Internet (On-line

Child Abuse). In such cases, being able to identify

the perpetrator as soon as possible is important due to

the serial habit of this type of criminal in committing

such crimes.

In this work, we propose to use the SPN signature

as a mean to ﬁnd social network accounts belonging

to the person that has shot a given photo. In other

words, we aim at answering the question: Given a

picture P

∗

; how can I ﬁnd an account belonging to

the person who shot P

∗

? We name this task Picture-

to-Identity Linking (see Fig. 2), and propose to ad-

dress it by using the SPN. To the best of our knowl-

edge, the task above has never been proposed in the

literature so far. The rationale behind our proposal

is that social network accounts might contain pictures

taken with the digital imaging device(s) of the account

owner. Given a probe picture P

∗

, one can extract its

SPN and compare it against the SPNs of the pictures

from a social network account. If a match is found,

this means that the account contains a picture taken

by the same camera of P

∗

; in turn, the owner of the

account is likely to be the person who shot P

∗

Picture-to-Identity linking via SPN poses several,

OntheUsageofSensorPatternNoiseforPicture-to-IdentityLinkingthroughSocialNetworkAccounts

speciﬁc challenges. Among them we highlight:

• Differences in Image Size. The size of the probe

image P

∗

and of the social network images can

differ, e.g. because images uploaded to the social

network are automatically resized (e.g., this is the

case of Facebook). In turn, SPN sizes will differ.

• SPN Misalignment. The probe image P

∗

and/or

the social network images might have been

cropped by the author; consequently, the SPNs

might be misaligned.

• JPEG Compression. Most images taken with

digital imaging devices are compressed using

JPEG. Some social networks might further com-

press them automatically after uploading. JPEG

compression degrades the SPN as it attenuates or

destroys high frequency image components.

• Image Alteration. Images can be altered for

artistic purposes (e.g., contrast enhancement,

HDR processing, or other visual effects). Any

such image alteration in turn modiﬁes the SPN.

• Different ISO Settings. Images shot in low light

conditions will have a higher ISO, and thus show

a stronger noise. On the contrary, images taken in

good lighting conditions will have a low ISP and

less noise.

Despite the challenges above, we will demonstrate

that the Picture-to-Identity linking via SPN is feasi-

ble, and could be used as a tool for forensic investiga-

tions. A practical implementation of our proposal is

presented in the next Section.

4 METHOD OVERVIEW AND

IMPLEMENTATION

In this Section, we describe our method to perform

Picture-to-Identity linking via SPN. We formalise the

problem as follows. Let P

∗

be a probe (target) im-

age, and let us deﬁne I =





, i = 1, m the set of

m social network accounts A

that belong to the per-

son/identity I. Let then I =





, j = 1, k be a set of k

candidate identities, each associated with her own set

of accounts I

The problem of Picture-to-Identity linking can be

formulated as ﬁnding the identity I

∗

which owns the

account containing the image with highest matching

score to P

∗

(Fig. 2). Formally:

∗

= argmax

S(P

∗

) , I

∈ I (3)

where S(P

∗

,I) is the identity score, i.e., the maxi-

mum SPN matching score between the SPN extracted

from P

∗

and each image of each account own by iden-

tity I:

S(P

∗

,I) = max

A∈I

max

P∈A

ρ(n

∗

) (4)

In practice, due to the problems stated in Sect. 3,

the probability of wrong identiﬁcation may be high

(see next Sect. 5). Thus, in a practical implementa-

tion of the above technique, for a given probe P

∗

it is

better to show an ordered list of the candidate identi-

ties {I

∗

,. .. ,I

∗

}, ranked with respect to their score

S(P

∗

,I∗

For the extraction of the SPN, we used the addi-

tive model of Eq. (1) with the Wavelet-based ﬁlter

proposed by Lukas et al. (Lukas et al., 2006). The

reliability of the SPN is usually better in the central

portion of the image (Li and Satta, 2012; Li and Satta,

2011). Thus, before extracting the SPN, the image is

cropped by taking a 256 × 256 pixels window in the

image centre.

5 EXPERIMENTAL RESULTS

We evaluated the performance of the method above in

the task of Picture-to-Identity Linking, on a data set

of 1909 images, taken from social network accounts

and/or personal blogs belonging to 10 different iden-

tities. For each identity, we found two social network

accounts. All the accounts were publicly accessible

(i.e., not restricted to friends only). The number of

images per account, and the account type, are listed

in Table 1. Images vary in size and are mostly small.

In fact, in many social networks (e.g., Facebook) pic-

tures are automatically scaled to a small size to ﬁt

better to the web page layout. Other social networks

(e.g., Flickr) offer a low resolution preview of the im-

age, and bigger versions of the same picture can be

accessed by clicking on the preview. In these cases,

Table 1: Composition of the data set used for benchmark.

For each social network account, the number of images is

shown in brackets.

Identity Accounts (Nr. of images)

#1 Flickr (148), Personal blog (93)

#2 Facebook (105), Flickr (118)

#3 Flickr (93), Google + (70)

#4 Facebook (3), Flickr (199)

#5 Flickr (143), Tumblr (26)

#6 Flickr (98), Personal blog (21)

#7 Flickr (112), Google + (99)

#8 Facebook (84), Flickr (109)

#9 Facebook (12), Flickr (192)

#10 Facebook (11), Flickr (173)

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

Table 2: Picture-to-Identity confusion matrix.

Estimated identity

#1 #2 #3 #4 #5 #6 #7 #8 #9 #10

Real identity

#1 138 20 9 10 6 7 4 7 1 39

#2 25 107 9 19 13 7 15 7 9 12

#3 4 7 115 5 4 6 3 14 2 3

#4 12 20 12 97 15 8 6 15 8 9

#5 14 13 7 17 61 16 9 9 8 15

#6 10 14 15 8 13 36 4 11 3 5

#7 0 10 1 2 2 0 189 0 4 3

#8 8 13 21 18 9 17 6 85 10 6

#9 1 7 5 4 4 2 2 6 161 12

#10 38 18 7 9 13 5 2 4 13 75

we took the preview image only. Before extracting

the SPN, and in order to make them comparable, each

image is (i) rotated so that it has a landscape orienta-

tion (if necessary) and (ii) resized so that the long side

has a length of 4096 pixel, keeping the aspect-ratio.

Experiments have been carried out as follows.

Each image of the data set was taken out from its

source account and used as the query image P

∗

; then,

the identities were ranked with respect to the identity

score, computed by means of Eq. (4).

In Table 2 we show the identiﬁcation performance

in terms of confusion matrix. The (i, j)-th element in

the matrix is the number of images coming from an

account belonging to the identity i that has been clas-

siﬁed as belonging to the identity j. The ranking per-

formance, instead, has been assessed by means of Cu-

mulative Matching Characteristic (CMC), and of Syn-

thetic Recognition Rate (SRR) curves. The former is

the cumulative probability of ﬁnding the correct iden-

tity within the ﬁrst n ranks; the latter is the probability

of a correct recognition given m target identities. The

SRR curve can be obtained from the CMC (hence the

term synthetic) as SRR(m) = CMC(k/m) where k is

the number of identities (Gray et al., 2007). The re-

sulting plots are shown in Fig. 3 and Fig. 4, respec-

tively. The CMC and SRR curves corresponding to a

random guess are shown as well for comparison.

The probability of correct recognition at the ﬁrst

rank is above 56%, which is far higher than the ran-

dom guess, but obviously not enough for a precise

identiﬁcation. One could argue that these scoring re-

sults do not qualify data as evidence for its admissi-

bility in court. It is correct to state that an evidence as

such shall be 100% attributable, as level of accuracy,

to a subject (e.g. the victim, the suspect, etc.). How-

ever, a ranked list of candidate identities can be valu-

able for the forensic practitioner in the investigation

phase (Casey, 2009). In fact, there are circumstances

where evidences lead to a pool of several people under

suspicion. For example, if law enforces are not able

to identify and seize cameras belonging to each of

the suspects, images taken from their social network

1 2 3 4 5 6 7 8 9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

n (rank)

Recognition rate

CMC curve, Picture−to−Identity linking

Random guess

Our method

Figure 3: Picture-to-Identity linking performance, in terms

of Cumulative Matching Characteristics curve, in a bench-

mark data set of 1909 images (see the text for details).

1 2 3 4 5 6 7 8 9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

m (number of targets)

Recognition rate

SRR curve, Picture−to−Identity linking

Random guess

Our method

Figure 4: Picture-to-Identity linking performance, in terms

of Synthetic Recognition Rate curve, in a benchmark data

set of 1909 images (see the text for details).

accounts could be used as a set of candidates to test

against the probe picture, using the ranking technique

proposed in this paper. Another scenario is when the

investigator knows, from other trails, that the person

of interest has certain characteristics (e.g., age, na-

tionality, or ethnic group); accordingly, he/she can ex-

clude the non-relevant identities in the ranked list re-

turned by the proposed technique. E.g., in all such

cases the proposed method would help to skim off

the number of candidate identities related to the case,

which, in conjunction with other facts, may speed up

the investigation and increase its accuracy.

For the sake of completeness, we also evaluated

the CMC and SRR curves in the task of associating

each image with the source account. In analogy with

the terminology utilised so far in the paper, we refer

to this task as Picture-to-Account linking. The cor-

responding plots are shown in Figs. 5-6. Note that,

in this case, the number of targets is 20 (equal to the

number of accounts), instead of 10 as in the previous

experiment.

OntheUsageofSensorPatternNoiseforPicture-to-IdentityLinkingthroughSocialNetworkAccounts

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

n (rank)

Recognition rate

CMC curve, Picture−to−Account linking

Random guess

Our method

Figure 5: Picture-to-Identity linking performance, in terms

of Cumulative Matching Characteristics curve, in a bench-

mark data set of 1909 images (see the text for details).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

m (number of targets)

Recognition rate

SRR curve, Picture−to−Account linking

Random guess

Our method

Figure 6: Picture-to-Identity linking performance, in terms

of Synthetic Recognition Rate curve, in a benchmark data

set of 1909 images (see the text for details).

6 CONCLUSIONS

In this paper, we presented a novel usage of SPN for

digital image forensics, as a mean to ﬁnd social net-

work accounts belonging to a certain person, who has

shot a given picture. This task, that we named Picture-

to-Identity linking, has many potential applications

in digital image forensics, e.g., identiﬁcation of per-

petrators of On-line Child Abuse, cyber-bullying, or

thefts of smart-phones. Yet, it is a challenging task,

due to differences in image size, misalignment of the

extracted SPNs, compression artefacts, and other is-

sues.

We proposed an implementation of Picture-to-

Identity linking using the Wavelet-based ﬁlter of

(Lukas et al., 2006) to extract the SPN, and Nor-

malised Cross Correlation to compare noise signa-

tures. We evaluated the performance on a benchmark

data set built using publicly accessible social network

accounts. The reported results are promising; further-

more, we see room for various improvements. A pos-

sible one, in order to tackle the problem of SPN mis-

alignment that may happen e.g. if some images have

been cropped, is to use a sliding window approach

to compare the SPN extracted from the probe im-

age to the ones extracted from account images. Also,

the performance could be improved by combining the

SPN information with other metadata (e.g., Exif data,

if available), or with other camera identiﬁcation tech-

niques (e.g., de-mosaicking artefacts, dust traces).

It is worth pointing out the limitations of the pro-

posed evaluation, which is restricted to a reduced set

of ten identities (and twenty social network accounts).

Indeed, Picture-to-Identity linking makes much more

sense with a large-scale data base of social network

accounts. The experimental evaluation provided can

be seen as a simulation of the case when a set of

“candidate” social network accounts has already been

found by other means. One of the directions of further

research will therefore be to prepare a large-scale test

bed for Picture-to-Identity linking, possibly improv-

ing the proposed technique as explained above.

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