A comparative analysis of Copy-move forgery detection algorithms

2022 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Farida Khursheed ◽  
Mohassin Ahmad
Keyword(s):  
Comparative Analysis ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Copy Move Forgery Detection

scholarly journals A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features

2021 ◽  
Vol 22 (2) ◽  
pp. 1177
Author(s):  
Ismail Taha Ahmed ◽  
Baraa Tareq Hammad ◽  
Norziana Jamil
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Image Features ◽  
Basic Process ◽  
Forgery Detection ◽  
Image Forgery ◽  
Detection Algorithms ◽  
The Past ◽  
Copy Move Forgery Detection ◽  
Digital Image Forgery

<span>Digital image forgery (DIF) is the act of deliberate alteration of an image to change the details transmitted by it. The manipulation may either add, delete or alter any of the image features or contents, without leaving any hint of the change induced. In general, copy-move forgery, also referred to as replication, is the most common of the various kinds of passive image forgery techniques. In the copy-move forgery, the basic process is copy/paste from one area to another in the same image. Over the past few decades various image copy-move forgery detection (IC-MFDs) surveys have been existed. However, these surveys are not covered for both IC-MFD algorithms based hand-crafted features and IC-MFDs algorithms based machine-crafted features. Therefore, The paper presented a comparative analysis of IC-MFDs by collect various types of IC-MFDs and group them rely on their features used. Two groups, i.e. IC-MFDs based hand-crafted features and IC-MFDs based machine-crafted features. IC-MFD algorithms based hand-crafted features are the algorithms that detect the faked image depending on manual feature extraction while IC-MFD algorithms based machine-crafted features are the algorithms that detect the faked image automatically from image. Our hope that this presented analysis will to keep up-to-date the researchers in the field of IC-MFD.</span>

Deep learning based algorithm (ConvLSTM) for Copy Move Forgery Detection

2021 ◽  
Vol 40 (3) ◽  
pp. 4385-4405
Author(s):  
Mohamed A. Elaskily ◽  
Monagi H. Alkinani ◽  
Ahmed Sedik ◽  
Mohamed M. Dessouky
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Image Features ◽  
Testing Time ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Important Challenge ◽  
Copy Move Forgery Detection ◽  
And Coping

Protecting information from manipulation is important challenge in current days. Digital images are one of the most popular information representation. Images could be used in several fields such as military, social media, security purposes, intelligence fields, evidences in courts, and newspapers. Digital image forgeries mean adding unusual patterns to the original images that cause a heterogeneity manner in form of image properties. Copy move forgery is one of the hardest types of image forgeries to be detected. It is happened by duplicating part or section of the image then adding again in the image itself but in another location. Forgery detection algorithms are used in image security when the original content is not available. This paper illustrates a new approach for Copy Move Forgery Detection (CMFD) built basically on deep learning. The proposed model is depending on applying (Convolution Neural Network) CNN in addition to Convolutional Long Short-Term Memory (CovLSTM) networks. This method extracts image features by a sequence number of Convolutions (CNVs) layers, ConvLSTM layers, and pooling layers then matching features and detecting copy move forgery. This model had been applied to four aboveboard available databases: MICC-F220, MICC-F2000, MICC-F600, and SATs-130. Moreover, datasets have been combined to build new datasets for all purposes of generalization testing and coping with an over-fitting problem. In addition, the results of applying ConvLSTM model only have been added to show the differences in performance between using hybrid ConvLSTM and CNN compared with using CNN only. The proposed algorithm, when using number of epoch’s equal 100, gives high accuracy reached to 100% for some datasets with lowest Testing Time (TT) time nearly 1 second for some datasets when compared with the different previous algorithms.

scholarly journals False Matches Removing in Copy-Move Forgery Detection Algorithms

2020 ◽  
Vol 31 (1) ◽  
pp. 47
Author(s):  
Muthana Salih Mahdi ◽  
Saad N. Alsaad
Keyword(s):  
Image Processing ◽  
Social Media ◽  
False Positives ◽  
Scientific Journals ◽  
Forgery Detection ◽  
Sift Algorithm ◽  
Detection Algorithms ◽  
Key Points ◽  
Technology Age ◽  
Copy Move Forgery Detection

Today the technology age is characterized by spreading of digital images. The most common form of transfer the information in magazines, newspapers, scientific journals and all types of social media.  This huge use of images technology has been accompanied by an evolution in editing tools of image processing which make modifying and editing an image is very simple. Nowadays, the circulation of such forgery images, which distort the truth, has become common, intentionally or unintentionally. Nowadays many methods of copy-move forgery detection which is one of the most important and popular methods of image forgery are available. Most of these methods suffer from the problem of producing false matches as false positives in flat regions. This paper presents an algorithm of the Copy-Move forgery detection using the SIFT algorithm with an effective method to remove the false positives by rejecting all key-points in matches list that own a neighbor less than the threshold. The accuracy of the proposed algorithm was 95 %. The experimental results refer that the proposed method of false positives removing can remove false matches accurately and quickly.

scholarly journals Nonoverlapping Blocks Based Copy-Move Forgery Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yu Sun ◽  
Rongrong Ni ◽  
Yao Zhao
Keyword(s):  
Computational Complexity ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Smooth Part ◽  
Block Based ◽  
Copy Move Forgery Detection ◽  
High Computational Complexity

In order to solve the problem of high computational complexity in block-based methods for copy-move forgery detection, we divide image into texture part and smooth part to deal with them separately. Keypoints are extracted and matched in texture regions. Instead of using all the overlapping blocks, we use nonoverlapping blocks as candidates in smooth regions. Clustering blocks with similar color into a group can be regarded as a preprocessing operation. To avoid mismatching due to misalignment, we update candidate blocks by registration before projecting them into hash space. In this way, we can reduce computational complexity and improve the accuracy of matching at the same time. Experimental results show that the proposed method achieves better performance via comparing with the state-of-the-art copy-move forgery detection algorithms and exhibits robustness against JPEG compression, rotation, and scaling.

scholarly journals Copy-Move Forgery Detection Based on Pyramid Correlation Network

CONVERTER ◽  
2021 ◽  
pp. 745-755
Author(s):  
Peng Liang, Et al.
Keyword(s):  
Image Representation ◽  
Small Region ◽  
Spatial Layout ◽  
Correlation Network ◽  
Data Sets ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
The Poor ◽  
Block Based ◽  
Copy Move Forgery Detection

Block-based image copy-move detection algorithms disregard the spatial layout of the features, leading to the poor detection performance under small-region tampering samples. Therefore, we propose a pyramid correlation network (PCNet) for copy-move forgery detection, whose goal is to obtain rich and detailed image representation via a pyramid cascaded correlation architecture. Experimental results show that PCNet outperforms the comparison algorithm on USCISI, CASIA and CoMoFoD data sets. Compared to the benchmark model BusterNet, F1 scores of PCNet has increased by 33.84% and 30.62% on CASIA CMFD dataset and CoMoFoD dataset respectively.

scholarly journals Performance Evaluation of Distance Metric for Copy Move Forgery Detection

2021 ◽  
Vol 23 (08) ◽  
pp. 457-461
Author(s):  
Sudhakar K ◽  
◽  
Dr.Subhash Kulkarni ◽  
Keyword(s):  
Performance Evaluation ◽  
Experimental Results ◽  
Distance Metrics ◽  
Distance Metric ◽  
Forgery Detection ◽  
Detection Algorithms ◽  
Considerable Impact ◽  
Copy Move Forgery Detection ◽  
Detection Speed

This paper presents the performance evaluation of various distance metric in copy move forger detection algorithms. The choice of distance metric affects the detection speed. The proposed approach is tested over 9 different distance metrics. The experimental results found indicate the choice of distance metric has a considerable impact on forgery detection speed.

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