# Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera
Identification

Manisha, Chang-Tsun Li∗∗, Xufeng Lin, and Karunakar A. Kotegar Manisha and
Karunakar A. Kotegar are with the Department of Computer Applications, Manipal
Institute of Technology, Manipal Academy of Higher Education, Manipal, India
(e-mail<EMAIL_ADDRESS>karunakar.ak@manipal.edu).Xufeng Lin and
Chang-Tsun Li are with the School of Information Technology, Deakin
University, Waurn Ponds Campus, Geelong, Australia (e-mail:
{xufeng.lin,changtsun.li}@deakin.edu.au).∗∗Corresponding author: Chang-Tsun Li
(changtsun.li@deakin.edu.au).

###### Abstract

Source camera identification tools assist image forensic investigators to
associate an image in question with a suspect camera. Various techniques have
been developed based on the analysis of the subtle traces left in the images
during the acquisition. The Photo Response Non Uniformity (PRNU) noise pattern
caused by sensor imperfections has been proven to be an effective way to
identify the source camera. The existing image forensic literature suggests
that the PRNU is the only fingerprint that is device-specific and capable of
identifying the exact source device. However, the PRNU is susceptible to
camera settings, image content (e.g., scene details), image processing
operations (e.g., simple low-pass filtering or JPEG compression), and counter-
forensic attacks. A forensic investigator unaware of malicious counter-
forensic attacks or incidental image manipulations is at the risk of getting
misled. The spatial-synchronization requirement during the matching of two
PRNUs also represents a major limitation of the PRNU. In recent years, deep
learning based data-driven approaches have been successful in identifying
source camera models. However, the identification of individual cameras of the
same model through these data-driven approaches remains unsatisfactory. In
this paper, we bring to light the existence of a new robust data-driven
device-specific fingerprint in digital images which is capable of identifying
the individual cameras of the same model. It is discovered that the new device
fingerprint is location-independent, stochastic, and globally available, which
resolve the spatial synchronization issue. Unlike the PRNU, which resides in
the high-frequency band, the new device fingerprint is extracted from the low
and mid-frequency bands, which resolves the fragility issue that the PRNU is
unable to contend with. Our experiments on various datasets also demonstrate
that the new fingerprint is highly resilient to image manipulations such as
rotation, gamma correction, and aggressive JPEG compression.

###### Index Terms:

Image Forensics, Source Camera Identification, PRNU, Deep Learning,
Convolutional Neural Network.

##  I Introduction

The availability of cost-effective smartphones has made the creation of
digital images easier than ever before. As a result, digital images have
become ubiquitous. Sometimes, images are used for malicious purposes such as
fake news creation, pedo-pornography, and violence instigation. In such cases,
investigators may be interested in identifying the camera that has been used
to capture the image in question. However, one can easily manipulate the image
using freely accessible photo editing tools without leaving any visually
detectable traces. This reduces the credibility of the digital images to serve
as valid evidence in the court of law. In such a crime scene investigation,
source camera identification tools assist the forensic investigator to ensure
the trustworthiness of the digital image in question by identifying the origin
of the image. Albeit the fact that the information about the camera model,
date and time are available in the image header file (i.e., the metadata or
EXIF data), it cannot be used for forensic purposes as they can be easily
tampered with. As a result, blind approaches have been developed by
investigating the self-contained image data to identify the source camera,
rather than relying on the auxiliary metadata. The blind source identification
techniques take the advantage of the subtle traces left on the image by
various modules involved in the image acquisition pipeline. These traces carry
certain information that is unique to the source camera of the image and hence
can be used as device fingerprint.

Different camera models employ different lens systems to focus the light on
the sensor, resulting in a lens distortion in the image [1]. Each camera model
has a unique lens distortion pattern that helps in identifying the camera
model of the given image. Since the camera sensor can measure only one color
at each pixel location, a Color Filter Array (CFA) is used so that the
individual pixels only receive the light of a certain wavelength. The missing
color information is estimated from the neighboring pixels through a process
called demosaicing (interpolation). The camera models from different brands
have their own demosaicing algorithms to reconstruct the missing color values.
Thus, the inter-pixel dependencies caused by the CFA demosaicing have also
been used to identify the camera model of the image [2, 3, 4, 5]. The cameras
from different brands follow proprietary post-processing algorithms such as
white balancing and JPEG compression. Hence, the statistical traces left on
the image by the post-processing operations have been used to identify the
camera model of the image as well [6, 7].

However, the aforementioned intrinsic traces can only differentiate the
cameras of different brands or models and cannot trace back the individual
devices belonging to the same brand and model. Thus, it is necessary to
extract the features that can identify the exact camera. The imperfections in
the manufacturing of the camera sensor lead to variation in different pixels’
sensitivity to the light of the same intensity. As a result, each camera
leaves unique Sensor Pattern Noise (SPN) [8] in every image that it has
captured. The SPN has two major components: Fixed Pattern Noise (FPN) and
Photo Response Non-Uniformity (PRNU). The FPN and PRNU depend on dark current
and non-uniformity in the pixels of the camera sensor, respectively. The FPN
caused by the dark current in the camera sensor is usually suppressed using
the dark frame within the camera. Therefore, PRNU is the only dominant
component of the SPN that is present in the output image. The PRNU noise
pattern being a deterministic component, is stable over time and remains
approximately the same if multiple images of the same scene are captured [8].
It is independent of scene detail in the image and depends only on the
physical characteristics of the camera sensor. Thus, each camera is
characterized by its unique PRNU, which can be used as a device fingerprint
for differentiating individual cameras of the same model or brand [8]. Due to
these distinct characteristics, the PRNU has drawn much attention in the
device-level source camera identification.

Over the past two decades, a variety of methods have been proposed to identify
the source camera by extracting the PRNU from the images [9, 10, 8, 11]. Since
the PRNU resides in the high-frequency band of the image, most of the source
identification techniques [12, 13, 14, 11] employ the denoising filter
proposed in [8] to extract the PRNU from an image. However, the PRNU, a form
of hand-crafted device fingerprint, can be attenuated by many factors, such as
scene details [15], PRNU filtering [12], periodical image processing
operations [13], cameras settings [16], etc., and can be easily removed to
hinder source identification through simple low-pass filtering [17, 18].
Moreover, the extracted PRNU pattern has the same pixel number as the original
image, which incurs high computational and storage costs for source-oriented
image clustering [19, 20, 21]. To overcome the computational complexity, the
PRNU is either extracted from a small portion of the image or formated as a
more compact representation [19, 22]. However, this results in the loss of
important features characterizing the source camera, thus compromising the
accuracy of source camera identification.

Although PRNU fingerprint has been proven to be an effective way to identify
the source camera, it may be undesirable for anti-forensic or criminal
attackers such as pedophiles or fake news creators who want to retain their
anonymity when sharing images. It would be desirable to unlink the images from
their source camera when anti-forensics is needed. Thus, various counter
forensic techniques have been developed that enabled eliminating or
suppressing the PRNU from images to anonymize images. Also, the
desynchronization operations such as rotation, cropping, resizing, and
filtering prevent the detection of PRNU fingerprint by disturbing the
relationship between the neighboring pixel and thereby hindering the correct
identification of the source camera. There is currently a range of approaches
in the literature [17, 23, 18, 24] that are effective in defeating state-of-
the-field source camera identification techniques by eliminating the PRNU
fingerprint. A forensic investigator having no idea of a counter-forensic
attack on the image would have a high probability of getting misled by the
attacker.

In recent years, inspired by the success of deep learning in computer vision,
image forensics researchers developed Convolutional Neural Networks (CNNs)
based data-driven systems [25, 26, 27, 28, 29, 30, 31] to identify the source.
The current state-of-the-art CNN-based approaches [32, 33, 34, 35, 36] provide
promising solutions to camera model identification. However, as we will
discuss in Section II, they are not capable of effectively identifying devices
of the same models or brands. Therefore, it is desirable to have a new data-
driven method for extracting device fingerprint that is capable of
differentiating individual devices of the same model. The major contributions
of the proposed work are as follows.

  1. 1.

We make evident the presence of a new non-PRNU device-specific fingerprint in
digital images and develop a data-driven approach using CNN to extract the
fingerprint.

  2. 2.

We show that the new device-specific fingerprint is mainly embedded in the
low- and mid-frequency components of the image, hence is more robust than the
fingerprint (e.g., PRNU) residing in the high-frequency band.

  3. 3.

We show that the global, stochastic, and location-independent characteristics
of the new device fingerprint make it a robust fingerprint for forensic
applications.

  4. 4.

We validate the robustness of the new fingerprint on common image processing
operations.

The remainder of the paper is structured as follows. Section II reviews
related works. Section III presents the proposed data-driven approach to
explore the presence of a new fingerprint. Experimental results and
discussions are given in Section IV. Finally, conclusions are drawn in Section
V.

##  II Literature Review

In light of the high sensitivity of PRNU (i.e., a form of hand-crafted device
fingerprint) to common image processing (e.g., low pass filtering and
compression) in source device identification (SDI), the past few years have
witnessed the successful attempts of taking a data-driven approach based on
CNN [25, 26, 27, 28, 29, 30]. Bondi et al., [25] proposed a model based on CNN
and SVM for camera model identification. The method aimed at learning the
camera model specific features directly from the image rather than depending
on the hand-crafted features. The proposed method was able to work on small
image patches (64$\times$64 pixels) with 93% accuracy on 18 different camera
models. Tuama et al., [26] developed a CNN model by modifying the AlexNet. A
pre-processing layer consisting of a high-pass filter was added to the CNN
model to reduce the impact of scene details. The CNN was trained on image
patches of size 256$\times$256 pixels to identify the model of the source
camera. A CNN-based robust multi-classifier was developed by Yao et al., [27]
to identify the camera model. The method used 256 non-overlapping patches of
size 64$\times$64 pixels extracted from the central portion of the image and
achieved a classification accuracy of nearly 100% over 25 camera models.
Freire-Obregón et al., [28] proposed a source camera identification method for
mobile devices based on deep learning. The method involved training the CNN
model using 256 image patches of size 32$\times$32 pixels extracted from each
image. The performance is degraded when the multiple cameras of the same brand
and model are considered. Huang et al., [29] developed a CNN model and
evaluated the effect of the number of convolutional layers on the performance
of the model. The work suggested that the deeper CNN can achieve better
classification accuracy. Also, the work showed that replacing the Softmax
layer in the CNN with the SVM classifier will improve the identification
accuracy. Wang et al., [30] developed a CNN model by modifying the AlexNet and
equipping it with a Local Binary Pattern (LBP) pre-processing layer to allow
the CNN to have more focus on the intrinsic source information (such as PRNU
[8], lens distortion noise pattern [1] and traces of color dependencies
related to CFA interpolation [2]) that is concealed in the image rather than
the scene details. The images were divided into non-overlapping patches of
size 256$\times$256 pixels to train the CNN model. The proposed method
achieved an identification accuracy of 98.78% over 12 camera models. Albeit
the encouraging fact that the afore-mentioned CNN-based methods are able to
effectively identify the model of the source camera of the image in question,
they are by no means capable of effectively differentiating individual cameras
of the same model.

In [32], Chen et al., proposed a method to investigate the task of brand,
model and device identification using a Residual network. The model was
trained by using one patch of size 256$\times$256 extracted from each image.
Despite having the good ability to identify the brand and model, it was able
to achieve only 45.81% accuracy when different devices of the same brand and
model were involved in the experiment. Yang et al., [33] developed a content-
adaptive fusion network to identify the source devices of small image patches
of size 64$\times$64\. The exact device detection accuracy, in this case, was
70.19% when only 3 devices of the same model were used. Ding et al., [34]
further improved the task of exact device identification by developing a
residual network with a multi-task learning strategy by taking advantage of
both hand-crafted and data-driven technologies. However, the model was able to
achieve only 52.4% accuracy on 74 cameras from the Dresden dataset and 84.3%
accuracy on 51 cameras from the Cellphone dataset, on image patches of size
48$\times$48 pixels.

In almost all the existing techniques, a large number of images are used to
train the CNN models for source identification. However, collecting a large
number of images is infeasible in realistic scenarios. Thus, Sameer et al.,
[35] addressed the task of source camera identification, with a limited set of
images per camera. A deep Siamese network was trained using a few shot
learning technique by considering pairs of 64$\times$64 pixel image patches
extracted from 10 images per camera. The method outperformed other state-of-
the-art techniques, with a few training samples. Mandelli et al., [36]
proposed a 2-channel based CNN that learns a way of comparing image noise
residual and camera fingerprint at the patch level. Also, the method
considered the scenario in which PRNU fingerprint and images are geometrically
synchronized. The method was effective in achieving the enhanced source
identification accuracy. However, it depends on the PRNU fingerprint for SDI.
Hence, it works only when there is no pixel misalignment between the PRNU
fingerprint or when the PRNU is not attacked.

Although many progresses in differentiating individual devices [32, 33, 34,
35, 36] using the data-driven approach have been made to address PRNU’s
fragility issue, the accuracy is still far from forensically satisfactory.
Thus, exact SDI remains a challenging task. Furthermore, the existing CNN
models for source identification work only when there is no pixel misalignment
is present in the images. However, image manipulations and counter forensic
attacks can defeat the state-of-the-art source camera identification
techniques by breaking the structural relationship between the neighboring
pixels and eliminating the PRNU noise embedded in the image. Moreover, such a
spatial synchronization requirement (i.e., all image patches need to be taken
from the same location) is in no way feasible if the original image has been
subjected to cropping or affine transformations such as scaling, rotation,
shearing, and translation, etc. In view of the vulnerabilities of PRNU to
counter forensic attacks and common image processing techniques, the research
question then arises: is there some form of robust device-specific fingerprint
other than PRNU in the image that is capable of differentiating individual
devices of the same model? To answer this question, in this work we propose
two simple yet effective methods, _down sampling_ and _random sampling_ , to
generate PRNU-free images for learning robust device-specific fingerprint. By
leveraging the generated PRNU-free images and the powerful learning capability
of deep residual neural networks, we are able to establish the presence of
robust device-specific fingerprint other than PRNU that can be used to
identify the exact source device of images.

##  III Methodology

As we aim for the proposed model to learn a robust device fingerprint, our
approach is to avoid the high-frequency bands of images. This is also to serve
the purposes of 1) proving that the new device fingerprint is PRNU-irrelevant
(i.e., its effectiveness is not attributed to the PRNU) and 2) enhancing the
new device fingerprint’s immunity to the interference of strong scene details
appearing in the high-frequency bands. Therefore, we present two different
ways of creating PRNU-free image patches in Section III-A and III-B to reveal
different characteristics of the new device fingerprint and to align the patch
formation methods naturally with the input requirement of the proposed deep
learning model (i.e., III-C). The two ideas of forming the PRNU-free image
patches are applicable to other learning models.

###  III-A Generation of PRNU-free Images with Down Sampling

The PRNU fingerprint is embedded in the high-frequency bands of images [8].
Hence, the removal of the PRNU fingerprint can be achieved by eliminating the
high-frequency components of the image. There are various filtering operations
for removing high-frequency components of the images. Since we will use ResNet
as the fingerprint extractor (see Section III-C), which require patches of 224
x 224 pixels as input, our first method for PRNU removal is down sampling (a
form of low-pass filtering), which serves not only the purpose of removing the
PRNU but also conforming to the ResNet architectural requirement. We apply the
bilinear filtering (low-pass filtering) to down-sample each image to
224$\times$224 pixels with 3 color channels. To ensure effective PRNU removal,
we evaluate the similarity between the noise residual extracted from the PRNU-
free image and the reference PRNU of its source camera using the widely
adopted Peak to Correlation Energy (PCE) [9], as depicted in Fig. 1. The
experiments in [9] suggested a PCE detection threshold of 50 to determine
whether two images are from the same source device or not. The greater the PCE
value is, the higher the likelihood that the two images are of the same
origin. Also, the counter-forensic attacks used to impede PRNU often fixed the
PCE detection threshold at 50 to ensure effective PRNU removal and thereby
unlinking the images from their source camera [17, 23]. Thus, in the proposed
work, we also use 50 as the threshold to determine whether the down-sampled
images have become PRNU-free or not.

The PCE evaluation requires the noise residual that serves as the PRNU
fingerprint of the image ($I_{D}$) and the reference PRNU fingerprint of its
source device ($D$). We use the wavelet-based denoising method [9] for noise
residual extraction and construct the reference PRNU ($\hat{F}_{nat}$) from
the noise residuals extracted from 50 natural images captured by the same
device. To calculate the PCE value, we up-sample the down-sampled PRNU-free
images to the same size as the reference PRNU for the evaluation of PCE. We
also consider the reference PRNU ($\hat{F}_{flat}$) generated using 50 flat-
field images. The flat-field images have fewer intensity variations which
result in the generation of near-perfect camera fingerprint, making the PCE
estimate more convincing. As we will see in Section IV-B, the down sampling
operation results in PCE values much lower than the threshold 50, indicating
the effectiveness of PRNU removal in the down-sampled image $\hat{I}_{D}$.

Figure 1: PRNU fingerprint removal through down sampling and removal
effectiveness evaluation based on PCE. Figure 2: The hybrid ResNet-SVM to
extract the new device-specific fingerprint other than PRNU and to identify
the source camera.

‘

###  III-B Generation of PRNU-free Images with Random Sampling

The patch formation approach based on down-sampling, can help our search for a
device fingerprint in the low and mid-bands of images. However, it preserves
the spatial relationship among the pixels within the patches and will not be
able to address the spatial synchronization issue that the PRNU is unable to
contend with. Therefore, in this second method, we propose to use random
sampling to remove the PRNU fingerprint in images while complying with the
ResNet architectural requirement. More importantly, it is intended to prove
that there is a form of device fingerprint that is location-independent,
stochastic, and globally present. Being a location-dependent and deterministic
component [8, 10], the PRNU fingerprint requires proper spatial
synchronization, thus forming image patches with pixels selected at random
causes desynchronization and hence prevents the PRNU from contributing to
source identification. Furthermore, the random sampling operation disrupts the
contextual and semantic information of the image, which is irrelevant to the
intrinsic fingerprint of the source device. By so doing, the model learns the
device-specific fingerprint while ignoring such irrelevant but interfering
information. For this purpose, we create patches of size
224$\times$224$\times$3 pixels by taking pixels from random locations either
from the original image $I_{D}$ or the down-sampled image $\hat{I}_{D}$,
thereby eliminating the structural relationship between the neighboring
pixels. Here, each pixel is used only once while forming the patches. If a
machine learning model is able to learn from the image patches formed by
taking the pixels at random for device-level source camera identification, we
make evident the global presence of location-independent, stochastic, and
PRNU-irrelevant device-specific fingerprint that can be successfully used for
image forensic applications.

###  III-C Learning Device-Specific Fingerprint

With the PRNU-free images generated using the aforementioned two methods, we
propose to use a hybrid system based on residual neural network (ResNet) [37]
and SVM classifier. ResNet is a variant of the CNN model with “shortcut/skip
connections” that allows constructing substantially deeper networks for
performance gain and has achieved state-of-the-art results on a range of
computer vision benchmarks. The ResNet model serves as a feature extractor to
extract device-specific fingerprint, which will then be used to train an SVM
classifier to classify the images to their exact source cameras.

The depth of the CNN is a crucial parameter [27, 29] that significantly
affects the performance of the source camera identification task. However,
building a deeper CNN is not as simple as stacking layers in the light of the
vanishing gradient problem for neural networks relying on gradient-based
training, e.g. back propagation. More specifically, a greater network depth
makes it increasingly difficult to propagate the gradients back to the earlier
layers of the network for weights update. As a consequence, the performance of
the CNN saturates or even starts degrading rapidly as the CNN network goes
deeper. Such an issue can be addressed by introducing skip connections between
layers [37], which add the outputs of earlier layers to the deeper layers and
thus allow uninterrupted gradient flow between layers in the network. This not
only tackles the gradient vanishing problem but also enables the reusability
of features at different layers [38], making it possible to learn both low-
level and high-level features. Thus, our strategy is to make use of the deeper
ResNet model to learn richer feature representation for extracting the device-
specific fingerprint.

Feature/fingerprint extraction plays a major role in the success of source
camera identification. It necessitates that extracted features exhibit
discriminative characteristics among different cameras while remaining a high
degree of homogeneity within the same class. The discriminative power of the
features/fingerprints can be boosted by increasing the number of stacked
layers. The ResNet model has many variants with a different number of layers
such as ResNet18, ResNet34, ResNet50, ResNet101, etc. With the increase in the
number of layers, the ResNet model can integrate multi-scale features from
multiple layers to enhance the features discriminative power. In this work, we
employ the ResNet model with 101 layers (ResNet101) as the backbone of our
device fingerprint extractor. The proposed ResNet-based device-specific
fingerprint extractor is shown on the left-hand side of Fig. 2.

In order to capture the device-specific fingerprint other than PRNU, we train
the ResNet101 model using PRNU-free images as described in Section III-A and
III-B. The number of neurons in the last fully connected (FC) layer is equal
to the number of devices $D$ used for training the model. During the forward
propagation, the PRNU-free images are passed through successive ResNet blocks
and the GAP layer. Eventually, at the FC layer, the feature vector is reduced
to the number of cameras $D$ to get $z=[z_{1},z_{2},....z_{D}]$. Finally, a
Softmax function is applied to convert the scores produced by the FC layer to
probabilities $(\hat{\bm{y}}=[\hat{y}_{1},\hat{y}_{2},....\hat{y}_{D}])$,
where $\hat{y}_{i}$ is the probability of the image being taken with the
$i^{th}$ camera:

$\hat{y}_{i}=\frac{e^{z_{i}}}{\sum_{d=1}^{D}e^{z_{d}}}$ (1)

The prediction loss is then calculated based on the class distribution
($\hat{\bm{y}}$) predicted by the ResNet101 model and the one-hot encoded
ground-truth class vector ($\bm{y}$) of the input image. For the proposed
work, we make use of the categorical cross-entropy loss function which is
widely used for multi-class classification:

$L(\hat{\bm{y}},\bm{y})=-\sum_{i=1}^{D}y_{i}\log(\hat{y}_{i})$ (2)

where $y_{i}$ and $\hat{y}_{i}$ are the actual class and the probability of
the input image belonging to the $i^{th}$ camera, respectively.

###  III-D Source Device Identification

To make it evident that the device fingerprint extracted by the ResNet101
model can serve the task of SDI, we use the multi-class SVM classifier with
the Radial Basis Function (RBF) kernel to identify the source device for the
PRNU-free image. We feed the images to the trained ResNet101 model and use the
outputs from the GAP layer as the device-level fingerprints for training the
SVM classifier. Once the SVM classifier is trained, it makes class predictions
on the images in the test set based on the device fingerprint extracted from
the GAP layer to perform the task of SDI. We use the same training set (75% of
the images of each camera) for training both the ResNet101 model and the SVM
classifier, while the testing set (25% of the images of each camera) is only
used for testing the source camera identification performance of the SVM
classifier, which ensures that the testing set is not involved in the training
of feature extractor (i.e. the Resnet101 model).

##  IV Experiments and Discussions

###  IV-A Experimental Setup

For the evaluation of the proposed method, we have used the images from the
VISION dataset [39], Warwick Image Forensic Dataset [40], Daxing dataset [41]
and a custom-built dataset. All datasets have different characteristics in
terms of camera model and brand, image content and number of devices from the
same model. Because our work aims at identifying the device-specific
fingerprint, we have considered only the images taken from different devices
belonging to the same brand and model in these datasets. Details of the
cameras and the images used are given in Table I. From the VISION dataset, in
addition to natural images, we have also considered 50 flat-field images for
the generation of the reference PRNU ($\hat{F}_{flat}$). The custom-built
dataset includes images taken from two personal smartphone models. From each
model, we have considered two different devices. Involving different datasets
in the experiments ensures the diversity of the cameras and images used for
the evaluation. For each dataset, the images are randomly divided into the
training and testing sets, such that 75% of the images from each device are
randomly chosen for training the model and the rest 25% for testing the model.
We train the ResNet101 model for 20 epochs using the Adam optimizer with the
mini-batch size and the learning rate set to 12 and 0.001, respectively. All
experiments are conducted using Matlab2020b on an HP EliteDesk 800 G4
Workstation with an NVIDIA GeForce GTX 1080 GPU, 3.7 GHz Intel processor and
32GB RAM.

TABLE I: Devices in different datasets used in our experiments Dataset | Model |  | No. of
---
devices
| No. of images
---
of each device
| Resolution
---
Vision | iPhone 5c | 2 | 204 | 3264$\times$2448
iPhone 4s | 2 | 200 | 3264$\times$2448
iPhone 5 | 1 | 204 | 3264$\times$2448
iPhone 6 | 1 | 204 | 3264$\times$2448
Warwick |  | Fujifilm
---
X-A10
2 | 197 | 3264$\times$2448
Daxing |  | Huawei
---
P20
2 | 190 | 2976$\times$3968
iPhone 6 | 3 | 190 | 3264$\times$2448
iPhone 7 | 3 | 190 | 4032$\times$3024
| iPhone 8
---
Plus
1 | 190 | 4032$\times$3024
Oppo R9 | 2 | 190 | 2304$\times$4096
Xiaomi 4A | 5 | 190 | 3120$\times$4160
Custom-built |  | Redmi
---
Note 3
2 | 200 | 4608$\times$3456
| Asus
---
Zenfone
MaxProM1
2 | 250 | 4608$\times$3456

(a)

(b)

(c)

(d)

(e)

(f)

(g)

Figure 3: Results obtained with t-SNE on different datasets demonstrating the separation between different devices belonging to the same model: (a) two iPhone 5c (b) two iPhone 4s (c) two Fujifilm X-A10 (d) two Redmi Note 3 (e) two Asus Zenfone Max Pro M1 (f) five Xioami 4A and (g) 16 devices of 6 different models from the Daxing dataset. TABLE II: PCE evaluation for the PRNUs extracted from the original and down-sampled images w.r.t. $\hat{F}_{nat}$ and $\hat{F}_{flat}$ Device |  | Average PCE
---
of PRNUs of
30 original
images w.r.t.
$\hat{F}_{nat}$
| Average PCE of
---
PRNUs of
30 PRNU-free
images w.r.t.
$\hat{F}_{nat}$
| Average PCE of
---
PRNUs of
30 PRNU-free
images w.r.t.
$\hat{F}_{flat}$
iPhone 5c-1 | 16,067 | 1.52 | 2.19
iPhone 5c-2 | 9,848 | 1.96 | 2.93
iPhone 4s-1 | 17,674 | 1.22 | 1.89
| Fujifilm
---
X-A10-1
1,095 | 1.51 | -
| Redmi
---
Note 3-1
14,845 | 1.51 | -

###  IV-B SDI Based on Down-Sampled Patches

For each camera, the reference PRNU ($\hat{F}_{nat}$) utilized during the PCE
evaluation is estimated using 50 natural images. In the case of the VISION
dataset, we also generate the reference PRNU ($\hat{F}_{flat}$) using 50 flat-
field images. To evaluate the effectiveness of PRNU removal through image down
sampling, we compare the average PCE values over 30 test images before and
after removing the PRNU with the proposed PRNU removal method as detailed in
Section III-A. From the results in Table II, we can see that there is a
substantial reduction in the PCE values after removing the PRNU from the
images. Be it the reference PRNU $\hat{F}_{nat}$ estimated from natural images
or $\hat{F}_{flat}$ estimated from flat-field images, the average PCE values
for the down-sampled images are significantly less than the detection
threshold 50, indicating the effectiveness of the proposed down-sampling PRNU
removal method.

We further present our results on the establishment of the presence of a new
non-PRNU device-specific fingerprint. We have shown that down sampling is
effective in removing PRNUs. Thus, to demonstrate that device-specific
fingerprints can be effectively extracted from PRNU-free images, we first
down-sample all images in the training and testing sets to
224$\times$224$\times$3 pixels and then train the ResNet101 model on these
PRNU-free images from different devices of the same camera model. We carry out
various experiments by training the ResNet101 model on different datasets. The
devices used for each experiment are listed in Table III. The feature
extracted from the GAP layer of the trained ResNet101 model is
2048-dimensional. To visualize the high-dimensional features, we use the
t-distributed Stochastic Neighbor Embedding (t-SNE) and show the results in
Fig. 3. As we can see, the separation between the devices belonging to the
same brand and model is well evidenced, which suggests that _there is a unique
device-specific fingerprint other than PRNU in the images_.

TABLE III: SDI performance based on the non-PRNU fingerprint extracted from down-sampled and random-sampled images Exp. |  Device used | Testing Accuracy (%)
---|---|---
Down sampling from original images |  Random sampling from original images |  Random sampling from down- sampled images
1 |  | 2 iPhone 5c
---
97.06 | 83.86 | 84.44
2 |  | 2 iPhone 4s
---
98.00 | 85.70 | 86.38
3 |  | 2 Fujifilm X-A10
---
95.92 | 84.39 | 86.27
4 |  | 2 Redmi Note 3
---
97.00 | 85.38 | 92.26
5 |  | 2 Asus Zenfone
---
Max Pro M1
95.16 | 84.25 | 86.24
6 | 5 Xiaomi 4A | 99.57 | 94.01 | 95.79
7 |  | 16 devices
---
of 6 different
models from the
Daxing dataset
99.20 | 98.15 | 99.58
Figure 4: Normalized confusion matrix for SDI based on down-sampled images
extracted from original images for 16 devices of 6 different models in the
Daxing dataset.

Furthermore, the down sampling operation removes the high-frequency components
in the images, so only the low and mid-frequency components of the images were
taken into account by the ResNet101 model while learning the non-PRNU device
fingerprint. This indicates that the features that serve as _the device-
specific fingerprint extracted by the proposed model are mainly embedded in
the low and mid-frequency components of the image_. Since high-frequency
components of the image are sensitive to manipulation and counter forensic
attacks, we cannot rely on the device fingerprints concealed in the high-
frequency band for forensic purposes. However, the proposed method makes
evident the presence of the new device fingerprint in the low- and mid-
frequency band that can be used as the robust fingerprint to identify the
exact source device of the image.

Similar to the work in [25], we train an SVM classifier on the
2048-dimensional features extracted by the ResNet101 model to test their
capability in identifying the exact source device of the image. The
performance of the SVM classifier on exact device identification is evaluated
in terms of classification accuracy and is reported in the third column of
Table III. It can be observed that the proposed model is able to map the PRNU-
free images to their respective source cameras accurately based on the device-
specific fingerprint extracted by the ResNet101 model. Furthermore, the
proposed model achieves the best testing accuracy of 99.57% and 99.20% for
Experiment 6 and Experiment 7, respectively. Note that the 5 devices of the
Xiaomi 4A model in Experiment 6 and the 16 devices of 6 different models in
Experiment 7 are from the Daxing dataset, which includes flat-field images
such as the images of clear sky and white wall that are uniformly illuminated.
As flat-field images have less intensity variation, the proposed deep learning
model is less susceptible to scene details. This makes the model focus more on
the intrinsic information of the images which contributes to learning the
fingerprint. As a result, the proposed model achieved higher accuracy on the
Daxing dataset compared to the other datasets.

Figure 5: Normalized confusion matrix for SDI based on random-sampled images
extracted from original images for 16 devices of 6 different models in the
Daxing dataset. Figure 6: Normalized confusion matrix for SDI based on random-
sampled patches extracted from down-sampled images for 16 devices of 6
different models from the Daxing dataset.

###  IV-C SDI Based on Random-Sampled Patches

In this subsection, we evaluate the SDI performance of the new device-specific
fingerprint extracted from random-sampled patches, as detailed in Section
III-B. To study whether or not there are global stochastic device-specific
characteristics that can act as device fingerprints, from every image in the
training and testing sets, we extract 50 patches with pixels taken at random
from the original images or down-sampled images. The proposed hybrid ResNet-
SVM system is trained on these random-sampled patches to extract the non-PRNU
device fingerprint and identify the source device. We repeat the same
experiments in the previous subsection for the proposed random sampling method
and report the results in the fourth and fifth columns of Table III. Compared
to the results of down sampling, the SDI accuracy of random sampling drops
notably from about $95\%$ to $85\%$ for the cases of two devices. This is not
surprising as the local structural information of the original image is
abandoned for training. However, what is most striking is that the
identification accuracy ($>83\%$) of random sampling are much higher than that
of a random guess. Furthermore, the influence of the absence of structural
information tends to diminish as the number of devices increases in Experiment
6 and Experiment 7.

To allow for a more detailed comparison, we show the normalized confusion
matrices on the 16 devices of the Daxing dataset (Experiment 7) in Fig. 4-6.
We can see that the SDI performance only drops slightly for a few iPhone
devices, i.e. iPhone 7 and iPhone 8 Plus. These results appear to indicate
that involving more devices in the training is beneficial for learning
discriminative device-specific fingerprint. However, as we involve more
devices in the training, we should not overlook the potential chance of
learning model-specific rather than device-specific fingerprint, because the
number of camera models is usually larger than the average number of devices
of the same model in a dataset. Thus, further studies are required to
investigate the appropriate way of dataset construction for learning
discriminative device-level fingerprint. Another interesting observation from
Table III is that random sampling from the down-sampled images slightly
outperforms random sampling from the original images. We contend that most
high-frequency components removed by down sampling can be considered as
interference for device identification. Therefore, similar to the common
practice of suppressing the scene details to improve the performance of PRNU-
based source device identification [15], random sampling from the down-sampled
images rules out the effect of interferential high-frequency components in the
images and thus improves the device identification performance.

For random sampling, each patch is formed with pixels taken at random from the
image, the spatial relationship among the pixels within each of the patches is
disturbed. Therefore, no structural information (e.g., the model-specific
periodic artifacts due to JPEG compression or demosaicing) is maintained in
each patch. The results of random sampling in Table III suggest the existence
of stochastic and location-independent device-specific characteristics that
can be captured by random sampling to serve as device fingerprint. The PRNU
fingerprint, being a deterministic component, requires proper spatial
synchronization, post-processing such as resizing, cropping, and filtering of
the images prevent authentic source identification based on PRNU. Thus,
relying on the fragile PRNU for forensic purposes will mislead the
investigations. With the new characteristics being stochastic and globally
present, it opens the opportunity to learn robust device-specific fingerprint
that is less susceptible to the desynchronization operations and counter-
forensic attacks on the images.

###  IV-D Performance Comparison with State-of-the-Art Techniques

The existing literature on source camera identification [25, 26, 27, 28, 29,
30, 32, 33, 34, 35, 36] has shown excellent performance on camera model
identification. However, our preliminary analysis showed that the state-of-
the-art techniques on camera model identification cannot be used directly to
identify the exact device. Most, if not all, existing data-driven approaches
[25, 27, 28, 29, 30, 33] report a drastic reduction in the device-level
classification accuracy when PRNU-free images are used for training. In this
subsection, we compare the performance of the proposed hybrid ResNet-SVM
system with the state-of-the-art data-driven approaches. We train the state-
of-the-art techniques using PRNU-free images generated through the down
sampling operation. The classification accuracy achieved on the testing set of
different datasets is reported in Table IV. It can be observed that in the
case of individual device identification, both the proposed down sampling and
random sampling methods outperform the existing data-driven approaches by a
large margin. The content-adaptive fusion network [33] developed for
individual device identification exhibits a low classification accuracy. Also,
the CNN proposed in [30] that learns the intrinsic source information (such as
PRNU, lens distortion noise pattern and traces of color dependencies related
to CFA interpolation) through the local binary pattern (LBP) pre-processing
layer, shows a poor performance on PRNU-free images. Thus, their results
indicate that the PRNU remains an important characteristic for the existing
techniques to identify the source camera. They only work when a strong PRNU
fingerprint is present in the image. Nonetheless, it can be seen that _our
hybrid ResNet-SVM system is capable of identifying the individual cameras of
the same model from the PRNU-free down-sampled and random-sampled image
patches_.

We summarize two possible reasons that contribute to the superior performance
of our proposed method: 1) Deeper networks allow for learning more
discriminative features for SDI. However, some existing works, e.g. [25, 27,
28, 29, 30, 33], used shallow neural networks (less than 15 convolutional
layers) to learn features, which might not be sufficiently discriminative for
device-level source camera identification. 2) Probably inspired by the success
of PRNU, it is generally believed that the intrinsic source device information
largely resides in the noise residual or the high-frequency domain of an
image. For this reason, most existing data-driven approaches [42, 43, 44, 26,
30] attempt to learn discriminative features from the noise residuals or high-
pass filtered images rather than the original images. In contrast to these
approaches, our proposed methods successfully learn the device-specific
fingerprint from PRNU-free images, which indicates that PRNU noise may only be
one type, or make up a small part, of device-specific fingerprints.

TABLE IV: Performance comparison of proposed methods with state-of-the-art techniques. Method | Testing Accuracy (%)
---|---
| 2
---
iPhone 4s
| 2
---
Fujifilm
X-A10
| 2
---
Redmi
Note 3
| 16 devices
---
(Daxing
dataset)
|  CNN+SVM [25]
---
50.00 | 50.00 | 50.00 | 6.25
| CNN based multi-
---
classifier in [27]
76.00 | 58.16 | 75.00 | 76.60
| CNN with 6 layers
---
[28]
73.00 | 45.00 | 76.00 | 21.15
| CNN with 11 layers
---
[29]
50.00 | 50.00 | 50.00 | 6.25
| LBP+modified
---
AlexNet [30]
64.71 | 50.00 | 53.00 | 6.25
| Content-adaptive
---
fusion network [33]
70.00 | 59.00 | 62.73 | 51.06
| Proposed down
---
sampling from
original images
98.00 | 95.92 | 97.00 | 99.20
| Proposed random
---
sampling from
original images
85.70 | 84.39 | 85.38 | 98.15
| Proposed random
---
sampling from
down-sampled images
86.38 | 86.27 | 92.26 | 99.58
TABLE V: Identification accuracy (%) of the proposed method under various image manipulations Device used for the experiment | Original | Gamma Correction | Rotation | JPEG Compression
---|---|---|---|---
$\gamma=0.7$ | $\gamma=1.4$ | $\theta=15^{\circ}$ | $\theta=30^{\circ}$ | $\theta=90^{\circ}$ | $Q=90\%$ | $Q=50\%$ | $Q=20\%$
2 iPhone 5c devices | 97.06 | 97.06 | 96.08 | 96.08 | 87.25 | 91.18 | 97.06 | 96.08 | 96.08
2 Fujifilm X-A10 devices | 95.92 | 95.92 | 94.95 | 91.84 | 88.78 | 85.71 | 95.92 | 95.92 | 95.92
2 Redmi Note 3 devices | 97.00 | 97.00 | 94.00 | 97.00 | 94.00 | 83.00 | 97.00 | 97.00 | 96.00

###  IV-E Evaluation of Robustness Against Image Manipulations

In practical circumstances, the images are often subjected to various
manipulations, which requires the source identification techniques to be
robust against these manipulations. In this subsection, we test the robustness
of the proposed method against various image manipulations such as gamma
correction (with $\gamma{\in}\\{0.7,1.4\\}$), rotation (with rotation angle
$\theta{\in}\\{15^{\circ},30^{\circ},90^{\circ}\\}$), and JPEG compression
(with quality factor $Q{\in}\\{90\%,50\%,20\%\\}$), thereby evaluate the
capability of the new device fingerprint. To verify the robustness we have
considered the proposed system trained on the original down-sampled images
(i.e. no manipulations applied). For the testing process, all the images in
the test set are processed with the aforementioned image manipulations and
then down-sampled to remove PRNU fingerprint.

The SDI performance of the proposed method for various image manipulations on
different cameras are presented in Table V. It can be seen that the proposed
method is effective in identifying the images subjected to gamma correction.
The identification accuracy on the rotated images is slightly lower than that
on the original images. JPEG compression is widely used on social networks
which suppresses the PRNU fingerprint left on the high-frequency band of the
images making source identification a difficult task. However, it is evident
from the results that the proposed method is almost unaffected by the JPEG
compression attack on the images. This is not surprising because the new
device-specific fingerprint resides in the low and mid frequency band, so even
if the JPEG compression removes the high-frequency components in the image,
the proposed method is still effective to identify the source device of the
images.

##  V Conclusion

In this paper, we explore the existence of a new device-specific fingerprint
other than the PRNU in the low and mid-frequency bands of images for source
device identification. The presence of the new device fingerprint in the low
and mid-frequency bands of images is established by training a hybrid
ResNet101-SVM model on down-sampled and random-sampled image patches. While
the existing data-driven approaches can differentiate only camera brands or
models, the experimental results suggest that the proposed system is effective
in extracting the non-PRNU device-specific fingerprint and mapping the images
to the individual devices of the same model. The source device identification
based on the new device-specific fingerprint presented in this paper provides
significantly more accurate and reliable results compared to earlier data-
driven approaches that depend on the PRNU fingerprint. Moreover, the PRNU-
based approaches demand proper spatial alignment of the images to identify the
source camera. Nonetheless, the location independent and global stochastic
characteristics of our new device fingerprint that learned from random-sampled
patches make it possible to identify the source camera even with the presence
of spatial misalignment of the images. We have also investigated the
reliability of the new fingerprint from images subjected to gamma correction,
rotation and JPEG compression. It shows that the new fingerprint is effective
and robust in identifying the source camera of the images subjected to
aggressive JPEG compression. With these promising results, we establish the
presence of a robust device-specific fingerprint that can be used for image
forensic applications.

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