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>

Research Perception Towards Copy-Move Image Forgery Detection: Challenges and Future Directions

2021 ◽  
pp. 2150054
Author(s):  
Sai Pratheek Chalamalasetty ◽  
Srinivasa Rao Giduturi
Keyword(s):  
High Performance ◽  
Machine Learning Algorithms ◽  
Forgery Detection ◽  
Future Directions ◽  
Image Forgery ◽  
The Past ◽  
Image Forgery Detection ◽  
General Kind ◽  
Copy Move Forgery Detection ◽  
Future Direction

In digital images, Copy-Move Forgery is a general kind of forgery techniques. The process of replicating one part of the image within the same image is termed as copy-move forgery. An effective and reliable approach needs to be developed for identifying these forgeries for restoring the image trustworthiness. The main intent of this paper is to sort out the diverse copy-move image forgery detection models. This survey makes an effective literature analysis on a set of literal works from the past 10 years. The analysis is focused on categorizing the models based on transformation models, machine learning algorithms, and other advanced techniques. The main contribution and limitations of the works are clearly pointed out. In addition, the types of datasets and the simulation platforms utilized by different copy-move forgery detection (CMFD) models are analyzed. The performance measures evaluated by different contributions have been observed for making a concluding decision. The utilization of optimization algorithms on copy-move image forgery detection has also been studied. Finally, the research gaps and challenges with future direction are discussed, which is helpful for researchers in developing an efficient CMFD that could attain high performance.

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 Convolutional Neural Network for Copy-Move Forgery Detection

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1280 ◽  
Author(s):  
Younis Abdalla ◽  
M. Iqbal ◽  
Mohamed Shehata
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Digital Image ◽  
Forgery Detection ◽  
Image Forgery ◽  
Available Technology ◽  
Copy Move Forgery Detection ◽  
Digital Image Forgery

Digital image forgery is a growing problem due to the increase in readily-available technology that makes the process relatively easy. In response, several approaches have been developed for detecting digital forgeries. This paper proposes a novel scheme based on neural networks and deep learning, focusing on the convolutional neural network (CNN) architecture approach to enhance a copy-move forgery detection. The proposed approach employs a CNN architecture that incorporates pre-processing layers to give satisfactory results. In addition, the possibility of using this model for various copy-move forgery techniques is explained. The experiments show that the overall validation accuracy is 90%, with a set iteration limit.

Deep Learning on Digital Image Splicing Detection Using CFA Artifacts

2020 ◽  
Vol 12 (2) ◽  
pp. 31-44 ◽  
Author(s):  
Nadheer Younus Hussien ◽  
Rasha O. Mahmoud ◽  
Hala Helmi Zayed
Keyword(s):  
Digital Image ◽  
Feature Reduction ◽  
Color Filter ◽  
Forgery Detection ◽  
Image Splicing ◽  
Detection Scheme ◽  
Image Forgery ◽  
Image Splicing Detection ◽  
Splicing Detection ◽  
Digital Image Forgery

Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.

Image Spam

2017 ◽  
pp. 58-89
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Image Features ◽  
Spam Detection ◽  
Detection Algorithms ◽  
Color Features ◽  
Spam Filters ◽  
Image Spam ◽  
High Level

Spam features represent the unique and special characteristics associated with spam, which are further used to differentiate them from other genuine messages. Each message m is processed by a feature extraction module to represent m in terms of n dimensional feature vector x = (x1, x2, …, xn) containing n features. This feature vector consists of many such features extracted from spam. In case of text based spam filters, a feature can be a word and a feature vector may be composed of various words extracted from spam. Each spam is associated with one feature vector. Based on the characteristics discussed in previous chapter, we will try to extract different features capturing those unique characteristics from image spam, in order to build the robust spam detection algorithms further. These features are broadly classified into high level metadata features, low level image features like color features, grayscale features, texture related features and embedded text related features.

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