=Paper=
{{Paper
|id=Vol-3269/PAPER_07
|storemode=property
|title=Review of Copy-Move Image Forgery Detection
|pdfUrl=https://ceur-ws.org/Vol-3269/PAPER_07.pdf
|volume=Vol-3269
|authors=Amit Kumar,Namita Tiwari,Meenu Chawla
|dblpUrl=https://dblp.org/rec/conf/wins/KumarTC22
}}
==Review of Copy-Move Image Forgery Detection==
https://ceur-ws.org/Vol-3269/PAPER_07.pdf
Review of Copy-Move Image Forgery Detection
Amit Kumar a, Namita Tiwari a and Meenu Chawla a
a
Maulana Azad National Institute of Technology Bhopal, M.P, Bhopal, India
Abstract
In today's technology world, digital photographs serve a critical function in a variety of
fields. Using advanced photo editing tools, altering and rearranging the contents of a digital
image is a simple operation. Now it is possible to add, edit, or eliminate essential aspects
from such an image without behind any perceptible alterations. Copy-move forgery is now
the most frequent type of image manipulating in digital pictures, in which an item or region is
duplicated in the digital image. Forgery detecting and localization are two major areas of
study in digital forensics that have gotten a lot of attention. This paper reviews various
techniques for copy- move image forgery detection using the deep learning method.
Keywords 1
Image, Copy Move Forgery, deep learning, Convolutional neural network.
1. Introduction
Images are now utilized as one of the most valuable assets of information in different disciplines,
including medicine, education, digital forensics, health research, and sources of information. It's simple
to make a cast image with tools like Adobe, GIMP, Coral Draw, and Mobile applications like Image
Hacker. When a photograph has been used as evidence in courts of law, the authenticity of the image
becomes extremely important [1]. These created pictures have the possibility to have a great impact
on society and affect people's opinions. Social media campaigning has now become a new trend in
elections all over the world in recent years. On the plus side, digital images are often used to increase
election awareness. Simultaneously, faked photographs containing false information have been noticed
being shared on social media in an attempt to influence the public. Furthermore, some faked images
with misleading information concerning the COVID-19 epidemic have lately gone popular on social
media networks. [2]
[1] Digital image counterfeiting is one of the most widespread and developing criminal issues.
There are currently no adequate approaches for automatically verifying the trustworthiness of digital
photographs. Detecting fraud in digital photos is an emerging study area for verifying the legitimacy of
digital photographs. [3]. There are two types of digital image forgery detection approaches first is an
active method and the second one is a passive method. The active method retrieves features of the
image that is otherwise obscured. Watermarking and digital signatures are used to hide confidential
messages. In a picture, passive techniques identify the duplicate region, such as image splicing and
copy-move forgeries. There are two methods for detecting manipulation in the copy- move forgery
detection first one is the traditional technique and the second one is the deep learning technique.
However, the old approach does not function consistently over different manipulating methods. [4]
WINS-2022: Workshop on Intelligent Systems, April 22 – 24, 2022, Chennai, India.
EMAIL: amitbcebhagalpur@gmail.com (Amit Kumar)
ORCID: 0000-0002-2010-3707 (Amit Kumar)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
45
Figure 1: Copy-move Image forgery detection approach
2. Traditional Techniques
Paragraph In copy-move manipulating, an area of the picture (of any size) is picked for copy-move
action and placed in another section with the same picture. As a result, there will be a strong
association between such two areas. The copy-move tampering detection method's goal is to find
replicated portions in a given picture. The repetition is shown by the similarity (correlation) or
distance between characteristics derived from two separate sections of the picture. To retrieve region-
wise characteristics from the picture, researchers used the following methods: (i) the image is split
into tiny sections known as blocks, and characteristics for every block are retrieved, as proposed in
(ii) all of the image's keypoints are recognized, and characteristics for each keypoint are retrieved. To
generate similar blocks or similar keypoints pairs, the extracted characteristics are compared block-
by-block or keypoint-by- keypoint. If matching pairings are detected between two locations, the
duplication is confirmed, and the picture is categorized as tampered with. These methods are based on
the assumption that the modified region is large enough to hold numerous blocks or key points.[5]
suggested utilising the Fourier-Mellin Transform (FMT) to features extracted from picture frames.
Furthermore, the author makes an effort to minimize response time, which has increased detection
performance by employing counting bloom filters (CBF). the usage of the pixel matching concept, [6]
suggested a Discrete Wavelet Transform (DWT) for efficient detection of copy-move forgeries. The
approach is based on recursively analysing segmented sub-images in order to detect spatial-temporal
regions of copy-move image forgeries.
[7] used the Fourier Transform (FT) correlation coefficient as a measure of similarity between
picture blocks in log-polar form. [8] developed the PatchMatch method, which finds approximate
closest- neighbour matches among picture chunks efficiently.
[9] used a SIFT-based algorithm to build a method for detecting copy-move forgeries and
restoration. This technique was then improved by introducing an upgraded resilient clustering phase
based on the J-Linkage algorithm. These three innovative forensic detectors presented by [10] are
capable of eliminating global and regional key points, as well as abnormalities or inconsistencies in
key-point distribution following tampering. [17] presented speeding up robust features (SURF) as a
46
technique for detecting copy-move image forgeries, with SURF keypoints collected and matching
with the KD-Tree algorithm.
3. Deep Learning technique in CMFD
The investigation of the community of deep learners is now considered as a wide and evolving
network of researchers who have influenced each other through various ways or methodologies. Various
forensic researchers are seeking to apply a deep convolutional neural network as an image forgery
detecting method [18], and this technique is influencing digital image forensics as well. The technique
of learning an artificial neural network (ann) by layering deeper layers on top of each other is known
as deep learning (DL). Multi-layered information representations, which generally take the shape of a
neural network with far more than two layers, are the most essential element of deep learning. Such
strategies allow for the automated generation of data descriptors or characteristics at a higher level
based on the lower ones.[19] developed a median filtering detection technique based on a deep
learning approach depending on CNN, which allows the system to detect and retrieve features from
images dynamically. The suggested CNN varies from other standard CNN models in that the CNN's
initial layer architecture is a filtering layer. This filtration layer accepts a picture as inputs and outputs
of its residual filtering of the media (MFR). Using layers that alternate between convolution and
pooling, the approach gets various features for subsequent classification, allowing hierarchical
representations to be learned. Five commonly used picture datasets are put to use to assess the efficacy
among the suggested model. In comparison to current technology, approaches, the suggested method
exhibited considerable performance improvements, especially in order to identify copy-move image
forgery. In JPEG compression, the approach may also identify median filtering and tiny picture
chunks.
CNN used a deep learning technique for picture fraud detection, using RGB color images as input
to construct hierarchical representations automatically. The CNN approach was created by the author
to detect images that have been manipulated with using splicing and copy-move procedures. The
suggested technique has a unique feature in that it uses a simple high-pass filter set to initialize the
weight of the network's first layer, which is then used to calculate a spatially rich model's residual
maps. The suggested technique makes a few major contributions. A supervised CNN is first taught to
learn the information hierarchy aspects of the training image's modifying operations. The CNN's
initial convolutional layer acts as a pre-processing module, suppressing the influence of picture
contents as effectively as possible. The characteristics retrieved from an image are then used to scan
the whole picture using a patch-sizes sliding window. Finally, in the framework's final layer, the SVM
classifier is trained for binary classification using the generated feature representation, which might be
real or modified. The suggested technique outperforms certain current picture fraud detection methods
in terms of accuracy. [20] presented a CNN-based technique for copy-move image forgery detection.
According to the results of the experiments, the suggested approach produces a suitable fake picture
automatically generated via the use of the computer using a basic image under the copy-move
manipulation technique. The strategy, however, is not resistant to copy-move picture fraud in a real-
world scenario. Even if the suggested approach is not flawless, it is the first time CNN has been used
to identify copy-move forgeries, and it has become a pioneer for further research in this sector. [21]
employ a deep learning methodology for digital picture forgeries based on CNN, where the CNN
methodology was refined and it has been built expressly to enable the identification of the traces left
by the change. This approach is based on a modified traditional CNN architecture that includes a layer
of filtering to guarantee that the major content of the input picture is suppressed.
It is capable of reducing textures and edges that cause visual interference. After removing the
effect of unneeded data, It's feasible. to study the evidence left behind by the recommended smooth
filtration in this manner. On a range of public datasets, the suggested CNN-based model outperforms
several cutting-edge techniques, and it also performs well under a number of operational situations
such as Filtering techniques including bilateral, average, and Gaussian. To detect copy-move image
counterfeiting, a Convolutional Kernel Network (CKN) was developed. Based on data [22], It's a
47
patch- level CKN analysis. Among the most essential objectives in copy-move image forgery
detection is for the feature extraction to be resilient against specific feature alterations. The proposed
CKN method for copy-move image fraud detection and CKN based on GPU rebuilding, as well as the
proposed key point distribution based on segmentation method for trying to generate uniform
dispersion major facts and GPU-based adaptive over-segmentation, are all significant components of
(COB). According to the results of thorough testing, the recommended CKN outperformed hand-
crafted aspects and can also deliver great results when employing GPU-based CKN. [23] employed a
convolution neural network- based coherent framework called dual-domain-based convolution neural
networks (D-CNN). The suggested technique employs two sub-networks: Sub-SCNN and Sub-FCNN.
Both sub-networks are connected to locate the areas where a transfer technique is in operation. The
Sub-SCNN uses the statistical characteristics depending on three DWT levels as inputs to identify and
locate picture counterfeiting, whereas the Sub-FCNN uses statistical parameters based on these three
DWT frequencies as input. Using the characteristics of pre-trained Sub-SCNN and Sub-FCNN
networks, the recommended approach generated greater accuracy and avoided a significant
computational cost for such training stage whenever used to the D-CNN of the training stage.
Table 1: Comparison of image tampering detection methods based on deep learning
Tampering
Author Model Datasets Accuracy
Methods
Median filtering, Collected from 12
Bayar et al. [11] Gaussian CNN different camera 99.10%
blurring models
Double JPEG
Multi-domain
Amerini et al. [12] compression, UCID (1338 Images) 95%
CNN
Cut paste
15352 photos (NRCS
Median filtering, Photo Gallery,
Chen et al. [13] CNN 85.14%
Cut-paste BOSSbase 1.01, UCID,
Dresden, BOSS RAW)
Image Database of
Dresden (16k images 81% Detection Accuracy
Bondi et al. [14] Cut-paste CNN
from 26 different of localization is 82%.
cameras)
Columbia grey
Cut-paste,
Rao and Ni [15] CNN DVMM, CASIA v1.0, 98.04%
Copy- move
CASIA v2.0
Mask Regional 93 percent precision on
Copy-move, Convolution average (for cover) 97
Wang et al. [16] Cover, Columbia
Cut- paste Neural Network percent precision on
(Mask R-CNN) average (for Columbia)
4. Analysis and Findings
These are some key aspects discovered after a thorough examination of many study publications.
The detection of tampering job concentrates on coarse-grained image analysis, whereas the task of
localization concentrates on perfectly correct image processing. Tampering detection is more difficult
than locating the image's modified region. Researchers have developed a number of ways for detecting
tampering, but only a handful of them can pinpoint the modified region. Techniques for detecting
48
tampering that has been used in the past (both block-based and keypoint-based methods) rely on
scustom-made products characteristics. The DL approach can automatically analyze conceptual and
sophisticated features that are essential for identifying tampered regions. The DL models may be
utilized for (i) The input is classified as binary picture into genuine (original) and manipulated
classifications, as well as (ii) tampered region localization. CNN models were shown to have great
accuracy in the both classifying tampered photos and producing fine-grained masks for locating
tampered regions, according to the researchers. Deep training in networks, Alternatively, is challenging
and needs much computing power and influence a huge dataset. Because the individual analyzing the
manipulated image is unaware of the sort of forgeries used on the actual picture, detection that is
particular approach may not be effective. There is a need for a forgery detection technology that can
identify any sort of forgery. Researchers employ a variety of metrics to assess the efficacy of tamper
detection techniques (Receiver Operator, F-measure, accuracy, precision, recall Factors MCC, IoU,
ROC Curve, and so on). When evaluating the performance of various Algorithms for detecting
tampering, consistent criteria (measures) must be employed. On a collection of original (genuine) and
tampered photos, tampering detection's effectiveness methods are tested. The dataset must contain as
many distinct types of original photos as feasible, as well as a range of various tampering techniques,
in order to properly evaluate the algorithms. Several public datasets on image manipulation are
accessible. However, the size of these datasets is insufficient, limiting DL-based tampering detection
methods.
5. Conclusion
We presented a complete analysis of existing approaches for Copy-move detection image forgeries
in this study, including both traditional and deep learning methods. The relevance of the approaches
was reviewed, as well as the overall workflow or procedure of the method used. The essential
processes for traditional approaches are divided into two categories: block-based and keypoint-based.
Deep learning techniques are based on the principle of ensuring feature extraction in order to learn and
instantaneouslyfulfil classification. According to the results of this survey, several of the deep learning
fraud detection approaches outperformed other forgery detection systems. Furthermore, they are said
to be more efficient, particularly when GPU-based technology is used. However, there are still
significant drawbacks and limits to using deep learning to identify counterfeit. One of the drawbacks
is data since there are few databases for images relevant to CMFD, but deep learning techniques
demand a large amount of data, particularly for training and validation. Furthermore, the use of a deep
learning technique to CMFD has yet to be extended. However, deep learning is rapidly being used in
other domains such as object detection, diagnostic imaging, and remote sensing. Furthermore, picture
forgery detection has limits when used to real-world images, multioperation of manipulating images,
and homogeneous images. As a result, improved approaches are still needed to attain higher
performance, efficiency, and the ability to cope in conjunction with obstacles that remain in the
detection of image forgery using copy-move.
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