Common Gabor Features for Image Watermarking Identification

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

doi:10.3390/app11188308

Cited by 15 publications

(5 citation statements)

References 32 publications

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“…It can handle huge datasets that are highly dimensional. It improves the model's accuracy and avoids an overfitting problem [45].…”

Section: End For Endmentioning

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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“…It can handle huge datasets that are highly dimensional. It improves the model's accuracy and avoids an overfitting problem [45].…”

Section: End For Endmentioning

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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“…Following that, adding these values. I advise reading [26], [27] if you're interested to learn more about Gabor Features.…”

Section: Gabor Texture Featurementioning

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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“…DCA is the statistical method that was firstly proposed by [25]. Mainly, its objective is to produce a separate group of observations, relying on scores on quantitative predictor variables [27].…”

Section: Discriminant Analysis (Dca)mentioning

confidence: 99%

Real and Altered Fingerprint Classification Based on Various Features and燙lassifiers

Hameed¹,

Ahmed²,

Okashi³

2023

Computers, Materials &Amp; Continua

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Biometric recognition refers to the identification of individuals through their unique behavioral features (e.g., fingerprint, face, and iris). We need distinguishing characteristics to identify people, such as fingerprints, which are world-renowned as the most reliable method to identify people. The recognition of fingerprints has become a standard procedure in forensics, and different techniques are available for this purpose. Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models. Therefore, we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones. In order to improve fingerprint classification accuracy, our proposed method used the most effective texture features and classifiers. Discriminant Analysis (DCA) and Gaussian Discriminant Analysis (GDA) are employed as classifiers, along with Histogram of Oriented Gradient (HOG) and Segmentation-based Feature Texture Analysis (SFTA) feature vectors as inputs. The performance of the classifiers is determined by assessing a range of feature sets, and the most accurate results are obtained. The proposed method is tested using a Sokoto Coventry Fingerprint Dataset (SOCOFing). The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times. Three distinct degrees of obliteration, central rotation, and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints. The proposal achieved massive success with a classification accuracy reaching 99%. The experimental results indicate that the proposed method for fingerprint classification is feasible and effective. The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy.

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Common Gabor Features for Image Watermarking Identification

Cited by 15 publications

References 32 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

Real and Altered Fingerprint Classification Based on Various Features and燙lassifiers

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