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

Ahmed, Ismail Taha; Hammad, Baraa Tareq; Jamil, Norziana

doi:10.11591/ijeecs.v22.i2.pp1177-1190

Cited by 12 publications

(5 citation statements)

References 54 publications

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“…A CNN's convolution layer performs dual functions of feature extraction and discrimination [23]. Deep learning model training is challenging and requires a large amount of knowledge and processing power.…”

Section: Pretrained Cnn Models Overviewmentioning

confidence: 99%

Examining the Performance of Various Pretrained Convolutional Neural Network Models in Malware Detection

Abdulazeez,

Ahmed,

Hammad

2024

Applied Sciences

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A significant quantity of malware is created on purpose every day. Users of smartphones and computer networks now mostly worry about malware. These days, malware detection is a major concern in the cybersecurity area. Several factors can impact malware detection performance, such as inappropriate features and classifiers, extensive domain knowledge, imbalanced data environments, computational complexity, and resource usage. A significant number of existing malware detection methods have been impacted by these factors. Therefore, in this paper, we will first identify and determine the best features and classifiers and then use them in order to propose the malware detection method. The comparative strategy and proposed malware detection procedure consist of four basic steps: malware transformation (converting images of malware from RGB to grayscale), feature extraction (using the ResNet-50, DenseNet-201, GoogLeNet, AlexNet, and SqueezeNet models), feature selection (using PCA method), classification (including GDA, KNN, logistic, SVM, RF, and ensemble learning), and evaluation (using accuracy and error evaluation metrics). Unbalanced Malimg datasets are used in experiments to validate the efficacy of the results that were obtained. According to the comparison findings, KNN is the best machine learning classifier. It outperformed the other classifiers in the Malimg datasets in terms of both accuracy and error. In addition, DenseNet201 is the best pretrained model in the Malimg dataset. Therefore, the proposed DenseNet201-KNN methods had an accuracy rate of 96% and a minimal error rate of 3.07%. The proposed methods surpass existing state-of-the-art approaches. The proposed feature extraction is computationally quicker than most other methods since it uses a lightweight design and fewer feature vector dimensions.

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Section: Pretrained Cnn Models Overviewmentioning

confidence: 99%

Examining the Performance of Various Pretrained Convolutional Neural Network Models in Malware Detection

Abdulazeez,

Ahmed,

Hammad

2024

Applied Sciences

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“…10, it is the percentage of all photos in that class that have been correctly categorized. Each term that can be determined using the aforementioned formulas is described in Table 4 [42].…”

Section: 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =mentioning

confidence: 99%

A Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiers

Ahmed,

Hammad,

Jamil

2024

IEEE Access

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Three frequent factors such as low classification accuracy, computational complexity, and resource consumption have an impact on malware evaluation methods. These challenges are exacerbated by elements such as unbalanced data environments and specific feature generation. To address these challenges, we aim to identify optimal texture features and classifiers for effective malware detection. The article outlines a method that consists of four stages: malware conversion to grayscale, feature extraction using (segmentation-based fractal texture analysis (SFTA), Local Binary Pattern (LBP), Haralick, Gabor, and Tamura), classification using (Gaussian Discriminant Analysis (GDA), k-Nearest Neighbor (KNN), Logistic, Support Vector Machines (SVM), Random Forest (RF), Extreme Learning Machine (Ensemble)), and finally the evaluation. Using the Malimg imbalanced and MaleVis balanced datasets, we assess classifier performance and feature effectiveness. Comparative analysis indicates that KNN outperforms other classifiers in terms of Accuracy, Error, F1, and Precision, while SVM and RF as runners-up. Gabor performs better in MaleVis, whereas the SFTA feature performs better under the Malimg dataset. The proposed SFTA-KNN and Gabor-KNN methods achieve 96.29% and 98.02% accuracy, respectively, surpassing current state-of-the-art approaches. Additionally, higher computing performance is achieved by using fewer dimensions when employing our feature extraction method.

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“…Each convolutional layer comprises multiple filters, also referred to as kernels, which are small windows that slide over the input data [32]. During the training phase, the weights of these filters are learned, and they function as feature extractors, identifying specific patterns, edges, and textures present in the input [33].…”

Section: Convolutional Layers and Their Functionalitymentioning

confidence: 99%

A Novel Levy Walk-Based Framework for Scheduling Power-Intensive Mobile Edge Computing Tasks

Younesi,

Fazli,

Ejlali

2023

Preprint

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

Cited by 12 publications

References 54 publications

Examining the Performance of Various Pretrained Convolutional Neural Network Models in Malware Detection

Examining the Performance of Various Pretrained Convolutional Neural Network Models in Malware Detection

A Comparative Performance Analysis of Malware Detection Algorithms Based on Various Texture Features and Classifiers

A Novel Levy Walk-Based Framework for Scheduling Power-Intensive Mobile Edge Computing Tasks

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