CNN-based fast source device identification
Sara Mandelli, Davide Cozzolino, Member, IEEE, Paolo Bestagini, Member, IEEE,
Luisa Verdoliva, Senior Member, IEEE, and Stefano Tubaro, Senior Member, IEEE
Abstract—Source identification is an important topic in image
forensics, since it allows to trace back the origin of an image. This
represents a precious information to claim intellectual property
but also to reveal the authors of illicit materials. In this paper
we address the problem of device identification based on sensor
noise and propose a fast and accurate solution using convolutional
neural networks (CNNs). Specifically, we propose a
2-channel-based CNN that learns a way of comparing camera fingerprint
and image noise at patch level. The proposed solution turns out to
be much faster than the conventional approach and to ensure an
increased accuracy. This makes the approach particularly
suit-able in scenarios where large databases of images are analyzed,
like over social networks. In this vein, since images uploaded on
social media usually undergo at least two compression stages,
we include investigations on double JPEG compressed images,
always reporting higher accuracy than standard approaches.
I. I
NTRODUCTION
I
N this paper, we tackle the problem of image source
de-vice identification. State-of-the-art solutions rely on photo
response non-uniformity (PRNU) [1], a characteristic noise
trace left by the camera sensor on all acquired images. PRNU
is caused by imperfections in the sensor manufacturing process
and represents a unique pattern noise associated to a certain
device. It can be used both to trace the exact origin of an
image and to establish its integrity [2], [3].
The classic pipeline is based on the availability of a number
of images coming from the same device in order to carry out a
reliable PRNU estimation. Then, the image under test can be
compared with the sensor fingerprint through denoising and
peak to correlation energy (PCE) computation [4]. However,
computing the PCE between a query image noise and a
finger-print might be computationally expensive, and may become a
major bottleneck when source identification requires scanning
huge databases of device fingerprints [5], [6], [7]. With the
increasing resolution of digital sensors, the search time may
become prohibitive even for a moderate fingerprint database.
S. Mandelli, P. Bestagini and S. Tubaro are with the Dipartimento di
Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano
20133, Italy (e-mail: name.surname@polimi.it).
D. Cozzolino is with the Dipartimento di Ingegneria Elettrica e Tecnologie
dell’Informazione, University Federico II of Naples, 80125 Naples, Italy
(e-mail: davide.cozzolino@unina.it).
L. Verdoliva is with the Dipartimento di Ingegneria Industriale, University
Federico II of Naples, 80125 Naples, Italy (e-mail: verdoliv@unina.it).
This material is based on research sponsored by DARPA and AFRL under
agreement number FA8750-16-2-0173. The U.S. Government is authorized to
reproduce and distribute reprints for Governmental purposes notwithstanding
any copyright notation thereon. The views and conclusions contained herein
are those of the authors and should not be interpreted as necessarily
rep-resenting the official policies or endorsements, either expressed or implied,
of DARPA and AFRL or the U.S. Government. This work was supported
by the PREMIER project, funded by the Italian Ministry of Education,
University, and Research within the PRIN 2017 program. Hardware support
was generously provided by the NVIDIA Corporation.
To manage large image databases some methods look for
compressed or alternative representations of the sensor noise.
One of the first methods introduces the concept of fingerprint
digest: for each query fingerprint, only pixels with the largest
magnitude are extracted [8]. Instead, in [9] the idea is to reduce
the resolution of PRNU values by quantizing them into a single
bit, while in [10] a composite fingerprint is introduced. In [11]
compression is obtained using random projections followed
by binary quantization of the projected values, and in [12]
this technique is investigated for JPEG images. Alternatively,
in [13] a tree-like search strategy is designed to split PRNU
matching problem into a series of sub-problems.
We consider an alternative approach to compare PRNUs
and test images by leveraging convolutional neural networks
(CNNs) to achieve good performance on small image patches.
Notice that relying on small patches makes PRNU-based
forgery localization possible. Moreover, it enables analyses
on social networks, where images are often available only
in cropped formats. CNNs have been already successfully
applied for camera model identification [14], [15], for tracing
the origin of an image in a social network [16] or for PRNU
anonymization [17]. However, less attention has been devoted
to PRNU-based device identification. Only recently, in [18]
sensor noise extraction has been improved by using a
residual-based deep learning process that replaces the standard agnostic
denoising procedure.
In this paper, we devise a data-driven approach for source
identification based on PRNU and consider the scenario in
which PRNU and images are geometrically synchronized.
Differently from [18], our goal is to replace the PCE test
in favour of an alternative method based on CNNs. Using
deep learning to compare patches is a powerful approach [19].
It has been recently proposed for forensics applications in
[20], [21], [22] by means of Siamese networks, in order to
learn distinctive data representations that highlight
camera-based artifacts. Unlike [19], our solution does not compare
directly raw image pixels, but work on noise residuals. In
our scenario, patches to be compared are extracted from the
estimated PRNU and the noise residual of the test image,
which do not possess any type of periodicity and are hard to
compress. For this reason, we avoid embedded representations
and rely on the whole original information, by proposing a
network architecture that learns the best similarity function for
source identification. Specifically, we design a 2-channel-based
CNN, which, through proper training, is able to learn the best
way to compare patches for device identification. We propose
a shallow CNN architecture which turns out to be faster
and more accurate than standard PCE approach. Furthermore,
deeper CNNs are also investigated, enabling to achieve even
better accuracy if processing time is not a constraint.
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Le
ak
yR
el
u
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C
on
v
2D
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⇥
5
⇥
5
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P
1
⇥
P
1
⇥
16
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P
2
⇥
P
2
⇥
64
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M
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P
o
ol
in
g
,3
⇥
3
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Le
ak
yR
el
u
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C
on
v2D
,64
⇥
5
⇥
5
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P
3
⇥
P
3
⇥
64
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1
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1
⇥
32
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W
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P
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2
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