Fake Image Identification using Image Forensic Techniques
Satyendra Singh, Rajesh Kumar
Departments of Electronics & Communication, JK Institute of Applied Physics and Technology, University of Allahabad,
Prayagraj, UP, India
Keywords: Image forensic, Photo Forgery Detection, Copy-move Image Forgery, Splicing
Abstract: These days’ digital images are a popular way of information sharing. it is known that the uses of images
have diverse areas such as news, magazine, medical, education and entertainment etc. this popularity of
digital images creates an opportunity for the researchers to ensure trustworthiness of an images. Due to all
these important utilities, the digital image has been successfully making it place in society. In this era of
digitalization image editing software is available in cheap mobile devices. That can generate fake image
easily. Fake images have been used by some organisations for influencing election, and violence and
rumour in the society. This paper focus on forgery detection techniques of fake images and understand the
usability of proposed technique for better accuracy of fake image detection.
1 INTRODUCTION
Digital image is a collection of picture
elements(pixel), digital images used in various areas
such as social media, news courtrooms,
entertainment, political campaigns and so on. The
use of digital image is growing very fast today,
digital image has become an important means of
sharing information, even in a small meeting or an
important event, we all use digital cameras or
camera enabled mobile devices. Capture every
moment and share the photo of that moment on
social media. But with the speed of digital images
are becoming popular among us, the trend of fake
image is also increasing. It is difficult to ensure the
integrity and authenticity of the image due to
tempering of the images.
Nowadays image manipulation application
software is easily available on cheap mobile devices.
And by using some powerful software to manipulate
image, without leaving obvious visual clues. Image
authenticity problems occur in application that is
courtroom, mass media and so on. Concerning
authenticity problems of digital images. Therefore,
image an important issue in forensic science.
The digital image manipulation is widely used in
bad aim and hiding original information of an image
has a long history. The history of photo forgery is
very long. Photo has been used by people for a long
time, but the image forgery has been used for the
bad purpose. In below Figure1 shown a photo of
Mao, Stalin, Hitler, Castro, Mussolini, and Brezhnev
times. In this figure photograph is manipulated for
better poses to erasing enemies or bottle of beer
(
Kim et al., 2012)
.
Figure 1: farid@cs.dartmouth.edu 1
In the times of Stalin, the image manipulation is
needed long hours of lengthy work in darkroom, but
in the present day any one can manipulate image in
few seconds by using photo editing software like
adobe photoshop that cannot be easily detected. The
authentication of photo tampering is necessary to
secure image communication process and
trustworthy of an image.
This picture was taken in the G20 summit in
Hamburg, Germany. The images that are given
bellow official picture of G20 summit and showing
US president Trump on the side lines or lots of
images showing US president and Vladimir Putin
meet first time. In Figure 2 is a fake image, making
the round on social media in 2017. Photo in Figure 3
is the original Getty image.
Singh, S. and Kumar, R.
Fake Image Identification using Image Forensic Techniques.
DOI: 10.5220/0010563000003161
In Proceedings of the 3rd International Conference on Advanced Computing and Software Engineering (ICACSE 2021), pages 95-100
ISBN: 978-989-758-544-9
Copyright
c
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
95
Figure 2: Fake image, making the round on social media
in 2017.
Figure 3: Original Getty image
1.1 Image Forgery
Photo forgery is describe as deleting, adding, and
changing few main features from digital image
without leaving any sign (
Qureshi et al., 2014)
. Image
forgery is created many challenges to ensure
trustworthy of an image.
Types of digital image forgery
Recent years, many ways have been employed to
temper with digital images. Several types of digital
photo tampering are existing.
Some common types of image forgeries are:
1. Copy-move photo tampering
2. Splicing
3. Image Morphing
4. Image Retouching.
1.1.1 Copy-Move Photo Tampering
The digital photo tampering technique is easy to
implement and difficult to detect (
Abidin et al., 2019)
.
In this tampering some features of a photo are copies
and pasted to another area in the same photo. In
digital photo copied part can be any types (
Qureshi,
2014)
. In figure 4 shown an example of copy move
forgery.
Figure 4: Copy move forgery image.
1.1.2 Splicing
Splicing is another type of technique of image
tampering. In this tempering of image forgery two or
more images are used for creating tampered photo
(
Tembe et al., 2017)
. In this technique some feature of
photo is copied from one photo and pasted to
another photo. Image splicing is difficult to detect
than copy move forgery (
Majumder et al., 2018)
.
Figure 5 shows an example of an image splicing,
here left side is natural photo and right side (b) is the
spliced photo.
Figure 5: Splice image.
1.1.3 Image Morphing
In morphing forgery, morphing is applied in two
images. In this technique shape of photo is change to
one form shape in another photo (
Elaskily et al.,
2017)
. Figure 6 an example of photo morphing photo
(a) is original photo of Hillary Clinton, photo (b) is
morphed photo and (c) is original photo of Donald
Trump.
ICACSE 2021 - International Conference on Advanced Computing and Software Engineering
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(A) (B) (C)
Figure 6: A is original image of Hillary Clinton, B
Morphed-image and C is original photo of Donald Trump.
1.1.4 Image Retouching
This is less dangerous type of photo forgery than
other types of photo forgery techniques. In this
technique original photo do not changes, but some
enhancement or reduces feature of original photo
such as light, background and color changing to
divert attention about an objective in photo. Image
retouching technique is popular among smart phone-
based camera users and magazine photo editors
(
Mankar et al., 2015
). Fig.7. is an example of image
retouching.
on title should be in 11-point bold with initial letters
capitalized, aligned to the left with a linespace
exactly at 12-point, hanging indent of 1,0-centimeter
and with an additional spacing of 10-point before
(not applicable right after a subsection title) and 10-
poi
Figure 7: Retouching image.
2 LITERATURE REVIEW
(Eduard et al., 2019) present a system for detection
of splicing forgery of digital image forgeries. In here
method based on VGG-16 CNN. Author use CNN
based method for classification of images in two
parts first one is original and second forgery. And
classification is used image fragments. In this paper
used CASIA dataset for experiment. In this dataset
images are partitioned into training and test sample
in the ratio of 80:20. All the images are additionally
compressed by using JPEG algorithm. Proposed
CNN based algorithm used on compressed images of
quality factor(Q). accuracy on Q=90 and Q=80 is
67.1% and 66.3% respectively. And in this paper,
the accuracy of proposed algorithm is 97.8% for
fine-tuned model and 96.4% accuracy for zero stage
trained. Limitation of this algorithm, it has narrow
range of accuracy. Where forge image is compressed
by JPEG compression algorithm.
(Ghoneim et al., 2018) in here, author describe a
system for medical photo tamper detection for smart
healthcare system. Author proposed system is used
for checking authenticity of an image and identify
that photos related to healthcare are not change. The
proposed forgery system consists of noise extraction
pattern, SVM and ELM classifier and the realization
of multiresolution regression filter. In this paper the
proposed system is tested on three different
databases, two databases CASIA 1 and CASIA 2 has
original photo and other one DDSM database
mammogram. The proposed method achieve
accuracy 98% for original photos and 84.3% for
medical photos. The method achieved best
performance, when add the score of two classifiers.
(Selvaraj et al., 2020) in this study, author
proposed an improved key point-based copy move
forgery detection system and using a sensitivity
based clustering approach. Author find that
sensitivity-based clustering performs well in
comparison to existing agglomerative clustering and
DBSCAN algorithm. The suggested approach is also
resistant to a variety of geometric attacks, such as
rotation, composition and scaling.
(Dixit et al., 2020) proposed method has proved,
if picture is obtained from various datasets with
varied features. This method has proven to be
successful in detecting forgeries. For image level
and pixel level detection, the suggested approach has
shown encouraging results. The proposed method
shows robustness against composite attacks.
Proposed approaches to detect tampered pictures
that can withstand and a wider range of distortion
parameters, such as non-affine transformations, in
the future.
(Meena et al., 2020) author describe the Fourier
Mellin Transform (FMT) and SIFT algorithm for
copy move forgery detection technique. The
proposed method shows very satisfactory results
under various geometric transformations because the
FMT and SIFT descriptors are rotation and scaling
invariant in nature. Author find that the proposed
technique works very good in some special
condition like scaling with factor 50% - 200%, and
compression in JPEG with a quality factor up to 20.
3 MATERIALS AND METHODS
There are some images and some methods to find
the images are fake or not.
Fake Image Identification using Image Forensic Techniques
97
3.1 About Image
These images have been taken from Iranian Missile
test images in the form of jpg. In figure 8 is the
original image of missile test and in figure 9 is the
tampered image of missile test.
Figure 8: Original image of Iranian missile test.
Figure 9: Fake image of Iranian missile test.
3.2 Methods
In the Experimental purpose Scale invariant feature
transformation (SIFT) is used for copy move tamper
detection. In this method some steps are applied
firstly give input original or forged image converts
input image data into SIFT features named sift
descriptor, in second steps this feature covert into
clustering SIFT then matching clustering results and
finally take decision on forged or original image.
Table 1: Comparison of various tampering Detection techniques
SSN Title Methods Detection
Domain
Advantage
1 Tampered and computer-
generated face photos
identification based on deep
learning (Dang et al., 2020).
Deep learning -
basedframework.
Tampered and
computer-
generated face
photos
detection.
Flexible, computationally
efficient, and robust against
imbalanced dataset.
2 Deep learning on digital photo
splicing detection using CFA
artifacts ( Hussien et al., 2020).
Deep learning
using color filter
array.
Digital image
splicing.
Accuracy is 95.5%
3 Detecting fake images on social
media using machine learning
(AlShariah et. al.).
Deep learning
technique via
CNN.
Detecting fake
image on social
media
Accuracy is 97%
4 Copy-move tamper detection
using SURF feature extraction
and SVM supervised learning
technique (Dhivya et al., 2020).
Speeded up
robust feature
(SURF) and
SVM
Copy move
forgery
Accuracy is 95.5%
5 Median filtering forensics in
digital photos based on
frequency-domain features (Liu
et al., 2017).
A novel
frequency domain
feature
Medium
filtering
detection
Reduce computing and merit for
mass processing data in real
time.
6 Copy-move forgery detection
using SIFT algorithm (Huang et
al., 2008).
SIFT Copy move
forgery
This method has good
performance on different types
of post photo processing (such
as rotation noise, scaling etc.)
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4 EXPERIMENTAL RESULTS
To validate our proposed method, we performed
experiment for detecting copy move forgery using
scale invariant feature transformation (SIFT)
algorithm. SIFT algorithm applied on copy move
forged image. Following two figures are show
experimental results. Figure 10 shows results of
DoG pyramid images and Figure 11 result of copy
move forge images.
Figure 10: Results of DoG pyramid images.
Figure 11: Result of detected feature of copy-move forged
images.
For experimentation purpose MATLAB 2015a
student version and window 10 operating system,
8gb RAM and processor intel core i5 has been used.
In above figure green mark region using key points
and blue mark region is accurate selected key points
approximation. In this figure green and blue mark
region show copy part of same image. SIFT is better
method to detect copy move forgery.
5 CONCLUSIONS
In this work various types of digital image
tampering identification techniques are studied and
tested. For testing copy-move tampered image
Scale invariant feature transformation algorithm has
been used and experimented and experimental
results show that it is better, as compared to another
image forgery detection techniques. The main aim of
this study is to be understanding the various image
forgery detection techniques. Further this study
helps to the beginners for understand fundamental
steps involved in digital image forensic.
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