Image Prefiltering in DeepFake Detection
Szymon Motłoch
1
, Mateusz Szczygielski
1
and Grzegorz Sarwas
2 a
1
Faculty of Electrical Engineering, Warsaw University of Technology, Warsaw, Poland
2
Institute of Control and Industrial Electronics, Warsaw University of Technology, Warsaw, Poland
Keywords:
Deepfake, Fractional Order Derivative, Image Preprocessing, SRM Filter.
Abstract:
Artificial intelligence, becoming common technology, creates a lot of new possibilities and dangers. An exam-
ple can be open source applications that enable swapping faces on images or videos with other faces delivered
from other sources. This type of modification is named DeepFake. Since the human eye cannot detect Deep-
Fake, it is crucial to possess a mechanism that would detect such changes.
This paper analyses solution based on Spatial Rich Models (SRM) for image prefiltering connecting convolu-
tional neural network VGG16 to increase DeepFake detection with neural networks. For DeepFake detection,
a fractional order spatial rich model (FoSRM) is proposed, which was compared with classical SRM filter and
integer order derivative operators. In the experiment, we used two different approximation fractional order
derivative methods: first based on the mask and second used Fast Fourier Transform (FFT). Achieved results
we also compare with the original ones and the VGG16 network with an additional layer added to select the
parameters of the prefiltering mask automatically.
As a result of the work, we questioned the legitimacy of using additional image enrichment by prefiltering
when using the convolutional neural network. Additional network layer gave us the best results from the
performed experiments.
1 INTRODUCTION
The emergence of convolutional neural networks al-
lowed their use in the field of image processing and
computer vision. Along with the convolution opera-
tion structures, a completely new range of possibili-
ties appeared, mainly supporting analyzing the vision
scene. Not only the classification of images, but most
of all the detection of objects on them has become a
problem that neural networks are able to cope with
even better than humans. The development of au-
toencoders (Garcia et al., 2017; Chen et al., 2017)
and the Generative Adversarial Network (GAN) (Yi
et al., 2019; Ledig et al., 2017) technology caused
that the neural network became capable to transform
the styles of a given image or replace part of the con-
tent of an image or video sequence. Like professional
painters, the developed methods can change the en-
tire structure of the image so that the result is be-
yond recognition for an average observer. The further
development of these methods resulted in creating a
DeepFake modification, enabling the replacement of a
face from the original photo or video with a face pro-
a
https://orcid.org/0000-0003-4113-2387
vided from another source. Swapped faces look very
realistic, often showing emotions or behaviors, mak-
ing the obtained results practically undetectable to the
human eye. Consequently, this technology has be-
come very dangerous. For example, stock exchange
quotations are heavily dependent on statements and
events attended by important and influential people.
The creation of these types of manipulated videos can
cause a sudden change in stock prices. Another ex-
ample of a threat is discrediting other people. One
possibility here is to substitute the person’s face in a
pornographic film. For example, the preparation of
such a video may affect the results of national elec-
tions.
The described situations show that the detection
of DeepFake modifications has become a crucial task
(Mirsky and Lee, 2021). As a result, there is an in-
crease in interest in this, which affects the emergence
of many solutions aimed at detecting changes in im-
ages or video sequences made by algorithms. Deep
neural networks have been trained for this problem
that perform well, but there is no solution for 100%
reliability. It is evident when trying to detect modi-
fications in images, where the achieved effectiveness
476
Motłoch, S., Szczygielski, M. and Sarwas, G.
Image Prefiltering in DeepFake Detection.
DOI: 10.5220/0010841200003124
In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP, pages
476-483
ISBN: 978-989-758-555-5; ISSN: 2184-4321
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

is lower than operating on video sequences. For this
reason, algorithms are constantly being improved that
allow for the best solution to this problem.
One of the methods for increasing the effective-
ness of the neural network presented in the litera-
ture is preliminary filtering of the input data, aimed
at strengthening the relevant information. Some pa-
pers confirm the efficacy of such action (Chang et al.,
2020; Han et al., 2021; Liu et al., 2020). However,
keep in mind that a significant portion of your fil-
ters or operations can backfire. Incorrectly used pre-
filtering may reduce the resolution of photos or re-
move information necessary from the point of view
of the neural network.
Based on the solution presented by (Chang et al.,
2020), who used Spatial Rich Models (SRM) con-
necting with VGG16 for the detection DeepFake
modifications in photos, we analyze various methods
of prefiltering images to determine their usefulness.
Apart from the classic SRM known from the litera-
ture, we propose a novel solution based on the frac-
tional order operator called Fractional order Spatial
Rich Model (FoSRM). Two different types of frac-
tional order derivative approximations were examined
to choose the best order of fractional operator and the
most efficient approximator. Achieved results we also
compare with the original ones and the VGG16 net-
work with an additional layer added to automatically
select the parameters of the prefiltering mask.
The organization of the document is as follows.
In the next chapter, the solutions and results obtained
in other works on this issue are described. Section 3
discusses the algorithms used in the research. In sec-
tion 4 described performed experiments and obtained
results. The last chapter summarizes our research.
2 RELATED WORK
In (Rana and Sung, 2020) and (Wang et al., 2018)
authors took several steps to improve the quality of
the input data. The first operation was to excise only
the face from the analyzed images. This step uses a
face detection algorithm for cutting out only the in-
dicated area to focus only on an essential region of
interest. After applying these operations, the cut im-
age is scaled to the same size.
In (Zi et al., 2021) as part of data preprocessing,
authors extract face landmarks to align all the faces in
a face sequence, which allows being robust on chang-
ing face orientations.
In (Chang et al., 2020) used the SRM filter for im-
age preprocessing. Such a procedure amplifies the
noise, which contains information that can improve
the effectiveness of the network. The applied filtering
operation made it possible to detect information that
was not visible in the RGB channels before use.
In (Han et al., 2021) also used the SRM filter as a
data preprocessing method in this problem. However,
in this case, the direct application of this operation
did not give satisfactory results. The authors of the
publication say that this filter can significantly impact
the detection of pasted objects, but not the detection
of DeepFake manipulation. The reason is the com-
plicated nature of such a transformation. As a result,
is achieved the opposite effect to that obtained in the
previously described work. This publication also pro-
poses a filter called ”learnable SRM”. It was shown
that applying such a method in a two-channel network
improved the obtained results. According to the au-
thors of the publication, this method increased the ef-
fectiveness of the neural network by 2% compared to
other proposed methods.
In (Zhang et al., 2018) was shown that using the
SRM filter is very useful for detecting pasted objects
in photos. Besides, this operation performed well in
the steganalysis task.
In (Younus and Hasan, 2020) confirmed that
detection of DeepFake modifications based on the
sharpening of edges is an effective method. The Haar
transform was used to strengthen the edges.
In (Liu et al., 2020) investigated several aspects
of input preparation for this problem. Their research
checked whether the excision of fragments containing
only facial skin is sufficient to detect modifications. It
was also verified whether the algorithm’s effective-
ness would change for the black and white versions
of the input photos. In addition, the effect of apply-
ing the L0 filter to minimize the gradient was inves-
tigated. The results obtained showed that the regions
containing the skin contain enough information to de-
tect facial modifications efficiently. The results were
similar to those obtained with standard full-face ex-
cision methods. It was also shown that for black and
white images, the effectiveness was very similar and
only slightly decreased in detecting modifications in
color picture. Using the L0 filter as a method of initial
filtering of images resulted in a significant deteriora-
tion of the results, reducing the AUC by 0.2 compared
to the tests carried out without using this filter.
In (Guo et al., 2020) proposed the Adaptive Ma-
nipulation Traces Extraction Network (AMTEN) as a
method to enhance the modifications found in the in-
put data. The results obtained in the research showed
that the applied data preprocessing method improved
the effectiveness of DeepFake transformation detec-
tion.
Image Prefiltering in DeepFake Detection
477

In this paper, the influence of SRM filters on the
operation of the convolutional neural network used
to detect DeepFake modifications in the photos will
be tested. The impact of using non-integer order
derivatives as a data preprocessing method will also
be investigated. It was decided to use such an op-
eration due to its ability to sharpen the edges of the
image, which are described as key in detecting this
type of modification. This type of mechanism also
implies the enhancement of high-frequency informa-
tion, which may contain information essential for this
problem.
3 ALGORITHMS
This section describes algorithms used for prepro-
cessing input to VGG16 (Simonyan and Zisserman,
2015). The reason for choosing this network model
was a possibility for comparing the results of the ex-
periment described in the article (Chang et al., 2020).
This study examined the effect of image enhancement
and one of the SRM filters on the detection perfor-
mance of DeepFake modifications. This paper will
extend the analysis of the influence of image prepro-
cessing algorithms on the neural network’s effectivity
in the analyzed problem.
3.1 SRM Filter
SRM filter can extract local noise from image (Zhou
et al., 2018). This method uses various types of high
pass filters. Before using the convolutional neural net-
work for steganalysis, this was the best method used
for solving this problem (Kang et al., 2019).
In this paper various types of SRM filters were
examined. Firstly, following directional masks of
sizes 3 ×3 were analysed: horizontal (3), vertical (1),
left-diagonal (2) and right-diagonal (4) (Reinel et al.,
2021). In experiments another type of SRM filter,
proposed in (Reinel et al., 2021) (5) was used.
1
2


0 1 0
0 −2 0
0 1 0


(1)
1
2


0 0 1
0 −2 0
1 0 0


(2)
1
2


0 0 0
1 −2 1
0 0 0


(3)
1
2


1 0 0
0 −2 0
0 0 1


(4)
1
4


−1 2 −1
2 −4 2
−1 2 −1


(5)
Another mask used in experiments is 5 × 5 pro-
posed in (Chang et al., 2020):
1
12





−1 2 −2 2 −1
2 −6 8 −6 2
−2 8 −12 8 −2
2 −6 8 −6 2
−1 2 −2 2 −1





. (6)
It was also decided to use several masks at the
same time. This solution is described in (Zhou et al.,
2018), where authors tested 30 different SRM filters
and showed that only three of them were satisfactory.
These were the filters presented in points 6, 5 and 3.
Figure 1 shows an example of the result of apply-
ing filters. Two photos from the left show the original
images (unmodified), and the other two were modi-
fied with DeepFake. The transformed form of the im-
ages resembles a collection of points that is not very
readable for the human eye. You can see the facial
features from the original photos, but the representa-
tion of these images is not convenient for a human.
3.2 Masks for Approximation of
Fractional Order Derivatives
In (Amoako-Yirenkyi, P. Appati, J.K. Dontwi,
2016) proposed a mask based on Riemann-Liouville
fractional-order derivative definition. The 5 ×5 gra-
dient mask looks as follows:









2α
√
8
α
8Γ(1−α)
α
√
5
α
5Γ(1−α)
0
−α
√
5
α
5Γ(1−α)
−2α
√
8
α
8Γ(1−α)
2α
√
5
α
5Γ(1−α)
α
√
2
α
2Γ(1−α)
0
−α
√
2
α
2Γ(1−α)
−2α
√
5
α
5Γ(1−α)
2α
√
4
α
4Γ(1−α)
α
Γ(1−α)
0
−α
Γ(1−α)
−2α
√
4
α
4Γ(1−α)
2α
√
5
α
5Γ(1−α)
α
√
2
α
2Γ(1−α)
0
−α
√
2
α
2Γ(1−α)
−2α
√
5
α
5Γ(1−α)
2α
√
8
α
8Γ(1−α)
α
√
5
α
5Γ(1−α)
0
−α
√
5
α
5Γ(1−α)
−2α
√
8
α
8Γ(1−α)









(7)
for the derivative in respect to x and








2α
√
8
α
8Γ(1−α)
2α
√
5
α
5Γ(1−α)
2α
√
4
α
4Γ(1−α)
2α
√
5
α
5Γ(1−α)
2α
√
8
α
8Γ(1−α)
α
√
5
α
5Γ(1−α)
α
√
2
α
2Γ(1−α)
α
Γ(1−α)
α
√
2
α
2Γ(1−α)
α
√
5
α
5Γ(1−α)
0 0 0 0 0
−α
√
5
α
5Γ(1−α)
−α
√
2
α
2Γ(1−α)
−α
Γ(1−α)
−α
√
2
α
2Γ(1−α)
−α
√
5
α
5Γ(1−α)
−2α
√
8
α
8Γ(1−α)
−2α
√
5
α
5Γ(1−α)
−2α
√
4
α
4Γ(1−α)
−2α
√
5
α
5Γ(1−α)
−2α
√
8
α
8Γ(1−α)








(8)
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
478

Figure 1: Results of applying SRM filters. The series of
photos marked with letters represent: A) 5 ×5 mask B) a
vertical directional mask C) left diagonal directional mask
D) horizontal directional mask E) right diagonal directional
mask F) 3 ×3 mask G) combination of 5 ×5 masks, 3 ×3
and the horizontal directional for the grayscale image.A
constant value enhanced the obtained images for better vi-
sualization.
for the derivative in respect to y, where Γ(z) is a
gamma function which is an extension of the factorial
function to complex numbers, a and α is a fractional
order of approximated derivative.
The composition of the final result is determined
by the dependence: z →
p
x
2
+ y
2
. In general, mask
elements are expressed by the following formulas:
Θ
x
(x
i
,y
i
) = −
α ·x
i
Γ(1 −α)

x
2
i
+ y
2
i

−α/2−1
,
(9)
Figure 2: Filtering results using an approximating mask that
approximates the fractional order derivative.
Θ
y
(x
i
,y
i
) = −
α ·y
i
Γ(1 −α)

x
2
i
+ y
2
i

−α/2−1
,
(10)
where −m ≤i ≤m or −n ≤ j ≤n for (2m+1)×(2n+
1) is the mask size for all m,n ≥ 1 and α is a constant
parameter that specifies the order of the derivative.
Figure 2 shows examples of the transformation
performed by this method for different derived or-
ders. The resulting photos obtained for different sizes
of derivative orders look very similar to each other.
However, there are some minor differences like edge
reinforcement.
3.3 Calculating Fractional Order
Derivatives with FFT
In (Sarwas and Skoneczny, 2019) was shown how to
calculate image fractional order derivatives based on
the Riemann-Liouville definition. This method uses
Image Prefiltering in DeepFake Detection
479

Figure 3: Results of fractional order derivative method
based on FFT.
the Fast Fourier Transform. The derivative formulas
for the x and y coordinates are as follows:
D
α
x
g = F
−1
(( jω
1
)
α
G(ω
1
,ω
2
)), (11)
D
α
y
g = F
−1
(( jω
2
)
α
G(ω
1
,ω
2
)), (12)
where F
−1
is an inverse two-dimensional continuous
Fourier transform operator, and G is a Fourier trans-
form. The final result was combined for both direc-
tions, producing and calculating as the dependence:
z →
p
x
2
+ y
2
.
Figure 3 shows an example of the results obtained
using this method. The same arrangement as in the
previous examples was adopted. For each derivative
row the edges are enhanced and the resulting photos
become darker as the derivative order increases.
3.4 Integer Order Derivatives
As part of the experiment, classical derivatives of the
integer order will also be tested. It was decided to
use the most popular approximations in the form of
the Sobel, Scharra, Prewitt, and Laplacian operators.
In this way, first and second order derivatives will be
calculated.
3.5 Additional Layers
Adding any filter before using the neural network is
similar to extending the model with additional con-
volutional layers. The difference is in the selection
of the filter weights. In the first case, they are pre-
determined and cannot be changed. In the second,
the initial value is random, and only in the learning
process, the network optimizes them for the best re-
sults. It was decided to extend the study of adding
to the model additional convolutional layers because
it gives a possibility for better evaluation of other al-
gorithms. Experiments tested the extra layer of the
following sizes: 3 ×3, 5 ×5, 7 ×7, and 9 ×9.
4 EXPERIMENTS
This section presents the description of performed ex-
periments. At the beginning is presented the factors
guided by selecting the data set, its description, and
preparation for use. Then, the detailed course of the
experiment and the learning chosen parameters are
described. In the end, the obtained results are listed
in the table and analyzed.
4.1 Dataset
One of the most extensive DeepFake datasets is
Celeb-DF, developed by Yuezun Li et al. (Li et al.,
2019). The second version of this collection con-
tains 590 original videos collected from YouTube and
5,639 videos with DeepFake modification. The qual-
ity of this data set is due not only to the number of
samples, but also to the realistic face manipulation,
which is confirmed by the relatively poor results of
the popular DeepFake detection networks.
The research focused on single images, which
required prior processing of the dataset containing
videos to extract individual frames. The Haar algo-
rithm was used to detect faces on images based on the
model for frontal view detection. It let cut them out
of those pictures. First, the films were divided into
three sets so that the faces in the test, validation, and
training groups were unique. Then, the same number
VISAPP 2022 - 17th International Conference on Computer Vision Theory and Applications
480

of frames from each movie was randomly selected,
and faces were cut from them to obtain the following
number of sets: 9103 images in the training set, 3145
in the validation set, and 3079 in the test set.
4.2 Parameters
The initial parameter values were taken from (Chang
et al., 2020). Images with a size of 128 ×128 pixels
were provided as input to the models and processed
using the described methods (SRM filters, fractional
order derivative approximation using a mask, fast
Fourier transform, and classical forms of approxima-
tion of integral derivatives). Additionally, random
mirror images of the image concerning the horizon-
tal and vertical axis for data augmentation were used.
For the VGG16 model training process, the SGD
optimizer with a constant decay coefficient of 1e−6
and a Nesterov moment of 0.9 was used. The initial
learning rate was adjusted experimentally to achieve
the best performance. The values ranged from 0.001
to 0.0001. Categorical crossentropy was chosen as
the loss function. Before starting the experiment, the
influence of the random factor was reduced by defin-
ing a constant seed value of the pseudorandom num-
ber generator. An early stop mechanism was used
during the training of the neural network. The pro-
cess was interrupted if the obtained results were not
improved on the validation set for a specified number
of epochs.
4.3 Results
The results of the experiment are presented in four
tables. Each of them in the first line contains the re-
sult obtained for the VGG16 model for a more conve-
nient comparison with the tested preliminary filtering
algorithm. The area presented the measure of the ef-
fectiveness of a given method under the ROC curve
(Receiver Operating Characteristics) marked as AUC
(Area Under the Curve). This measure was calculated
for the validation and test data set.
Table 1: Results of SRM filters in DeepFake detection.
Method
AUC
Val Test
VGG16 0.876 0.870
SRM 3 ×3 + VGG16 0.856 0.844
SRM 5 ×5 + VGG16 0.880 0.869
SRM - vertical + VGG16 0.898 0.885
SRM - right-diagonal + VGG16 0.910 0.905
SRM - horizontal + VGG16 0.895 0.894
SRM - left-diagonal + VGG16 0.906 0.910
SRM - mix filter + VGG16 0.891 0.888
Table 1 contains the results of model detection
with preliminary filtering in the form of SRM filters.
Only one mask (SRM 3 ×3) decreased the detection
efficiency of DeepFake modifications. This shows
that the local noise, enhanced by the filter, had a posi-
tive effect on the performance of the model. The best
results were obtained for masks with diagonal direc-
tions. The AUC was increased by 0.04 compared with
detection without pre-filtering.
Table 2 shows the results obtained for the
fractional-order derived methods. Before starting the
experiment, similar results of both algorithms were
Table 2: Results of fractional order derivative in DeepFake
detection.
AUC
Method
Val Test
VGG16 0.876 0.870
0.1 0.882 0.885
0.2 0.895 0.892
0.3 0.886 0.880
0.4 0.891 0.881
0.5 0.880 0.883
0.6 0.879 0.879
0.7 0.884 0.884
0.8 0.882 0.872
0.9 0.886 0.889
1.1 0.889 0.883
1.2 0.835 0.826
1.3 0.884 0.885
1.4 0.880 0.878
1.5 0.879 0.871
1.6 0.884 0.866
1.7 0.881 0.868
1.8 0.872 0.882
Derivative
approximation
mask
+
VGG16
1.9 0.882 0.882
0.1 0.829 0.845
0.2 0.875 0.877
0.3 0.866 0.869
0.4 0.884 0.881
0.5 0.888 0.885
0.6 0.876 0.878
0.7 0.879 0.876
0.8 0.861 0.854
0.9 0.848 0.854
1.1 0.824 0.820
1.2 0.807 0.807
1.3 0.779 0.774
1.4 0.782 0.779
1.5 0.783 0.761
1.6 0.680 0.693
1.7 0.669 0.676
1.8 0.744 0.737
Derivative
FFT
+
VGG16
1.9 0.658 0.676
Image Prefiltering in DeepFake Detection
481

expected. In the case of using the FFT, a downward
trend can be seen with the increase in the order of
the derivative. The derivative in the range of 0.2-0.7
improved the detection results, and for the remaining
rows, it deteriorated. The worst efficiency was ob-
tained for derivatives larger than 1. This is probably
related to the noise that appears above the order of 1
for the FFT approximation (see Figure 3). The second
method of calculating the incomplete order showed
very similar results in the studied range. Single drops
in effectiveness can be seen, but these were not greater
than 0.045 for the measure AUC. Ultimately, the two
algorithms did not bring many benefits in detecting
DeepFake modifications.
Table 3: AUC results for proposed integer derivatives Deep-
Fake detection methods.
AUC
Method
Val Test
VGG16 0.876 0.870
Sobel 0.881 0.876
Scharr 0.910 0.897
First derivative
Prewitt 0.881 0.878
Second derivative Laplacian 0.925 0.907
The results of the next experiment were placed
in Table 3. Pre-filtering was tested in the form of
a classical integer-order derivative. The approxima-
tions using the Sobel and Prewitt operators slightly
improved the detection efficiency. The Scharr oper-
ator fared much better, increasing the AUC score by
0.027. The second derivative achieved the most sig-
nificant increase in the accuracy of DeepFake modi-
fication detection compared to the experiment with-
out using any filter. The AUC results were 0.925 and
0.907 for the validation and test sets, respectively.
Table 4: AUC results for additional convolutional layer in
proposed DeepFake detection methods.
AUC
Method
Val Test
VGG16 0.876 0.870
3 ×3 + VGG16 0.908 0.888
5 ×5 + VGG16 0.912 0.900
7 ×7 + VGG16 0.922 0.923
9 ×9 + VGG16 0.877 0.887
The best results have been obtained by adding an
additional convolutional layer. They were placed in
Table 4. Each tested filter size improves network per-
formance. An extra 7×7 convolution layer turned out
to be better than all the previously tested prefiltering
algorithms.
5 CONCLUSION
This paper addresses the issue of DeepFake image
modification detection. As part of the work carried
out, solutions based on deep neural networks for de-
tecting this type of disturbance were analyzed, focus-
ing on suggestions for preliminary photo filtering.
As part of the research, various SRM filters
were compared with the proposed FoSRM based on
the fractional order derivatives implemented in two
forms: an approximation mask and a fast Fourier
transform. The operation of classical methods of de-
termining derivatives of the integer order were also
tested. To reliably evaluate the filters, it was also
examined what results could be obtained if different
sized of one convolution layer will be added to the
neural network. Prefiltering of the images were car-
ried out on the entire data set before starting the pro-
cess of training the neural networks. Optimal training
parameters were selected, and the results obtained for
a set of validation and test data were compared.
The analysis of the results showed that using the
fractional order derivative can rich input image as
well as other SRM filters. From the linear filers the
best efficiency had diagonal SRM and Laplacian. Sur-
prisingly, the mix of SRM masks degraded the ob-
tained results compared to the individual masks. On
the other hand, adding one layer to the neural network
provide the best solution. It can be concluded that in
the case of using convolutional networks, it makes no
sense to use additional linear and even nonlinear im-
age prefiltering. In the learning process, the neural
network automatically selects linear filters and exam-
ines their influence on the detection based on the con-
volution operation. On the other hand, nonlinear op-
erators are performed using the ReLU type activation
function or the MaxPooling operator, which is also
analyzed during the training process.
The use of a defined filter may prove effective in
more complex network architectures, where the net-
work is forced to extract multiple different sets of fea-
tures and then merge all together. An example of such
an architecture is a multi-stream network. These con-
clusions may motivate further research on the influ-
ence of prefiltering on DeepFake modification detec-
tion results.
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