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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Baraa Tareq Hammad
University of Anbar
Computer Science & IT Electrical & Electronics Engineering

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Contrast-distorted image quality assessment based on curvelet domain features Ismail Taha Ahmed; Chen Soong Der; Baraa Tareq Hammad; Norziana Jamil
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i3.pp2595-2603

Abstract

Contrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain features are the basis of NR-IQA-CDI architecture. Therefore, in this paper, the spatial domain features are complementary with curvelet domain features, in order to take advantage of the potent properties of the curvelet in extracting information from images such as multiscale and multidirectional. The experimental outcome rely on K-fold cross validation (K ranged 2-10) and statistical test showed that the performance of NR-IQA-CDI rely on curvelet domain features (NR-IQA-CDI-CvT) significantly surpasses those which are rely on five spatial domain features.
A comparative review on symmetric and asymmetric DNA-based cryptography Baraa Tareq Hammad; Ali Maki Sagheer; Ismail Taha Ahmed; Norziana Jamil
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2470

Abstract

Current researchers have focused on DNA-based cryptography, in fact, DNA or deoxyribonucleic acid, has been applied in cryptography for performing computation as well as storing and transmitting information. In the present work, we made use of DNA in cryptographic, i.e. its storing capabilities (superior information density) and parallelism, in order to improve other classical cryptographic algorithms. Data encryption is made possible via DNA sequences. In this paper, two cases utilizing different DNA properties were studied by combining the DNA codes with those conventional cryptography algorithms. The first case concerned on symmetric cryptography that involved DNA coding with OTP (one time pad) algorithms. Asymmetric cryptography was considered in the second case by incorporating the DNA codes in RSA algorithm. The efficiencies of DNA coding in OTP, RSA, and other algorithms were given. As observed, the computational time of RSA algorithm combined with DNA coding was longer. In order to alleviate this problem, data redundancy was reduced by activating the GZIP compressed algorithm. The present experimental results showed that DNA symmetric cryptography worked quite well in both time and size analyses. Nevertheless, it was less efficient than the compressed DNA asymmetric cryptography.
Low feature dimension in image steganographic recognition Ismail Taha Ahmed; Norziana Jamil; Baraa Tareq Hammad
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 2: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i2.pp885-891

Abstract

Steganalysis aids in the detection of steganographic data without the need to know the embedding algorithm or the "cover" image. The researcher's major goal was to develop a Steganalysis technique that might improve recognition accuracy while utilizing a minimal feature vector dimension. A number of Steganalysis techniques have been developed to detect steganography in images. However, the steganalysis technique's performance is still limited due to their large feature vector dimension, which takes a long time to compute. The variations of texture and properties of an embedded image are clearly seen. Therefore, in this paper, we proposed Steganalysis recognition based on one of the texture features, such as gray level co-occurrence matrix (GLCM). As a classifier, Ada-Boost and Gaussian discriminant analysis (GDA) are used. In order to evaluate the performance of the proposed method, we use a public database in our proposed and applied it using IStego100K datasets. The results of the experiment show that the proposed can improve accuracy greatly. It also indicates that in terms of accuracy, the Ada-Boost classifier surpassed the GDA. The comparative findings show that the proposed method outperforms other current techniques especially in terms of feature size and recognition accuracy.
A comparative analysis of image copy-move forgery detection algorithms based on hand and machine-crafted features Ismail Taha Ahmed; Baraa Tareq Hammad; Norziana Jamil
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp1177-1190

Abstract

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.
Forgery detection algorithm based on texture features Ismail Taha Ahmed; Baraa Tareq Hammad; Norziana Jamil
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 1: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i1.pp226-235

Abstract

Any researcher's goal is to improve detection accuracy with a limited feature vector dimension. Therefore, in this paper, we attempt to find and discover the best types of texture features and classifiers that are appropriate for the coarse mesh finite differenc (CMFD). Segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and Haralick are the texture features that have been chosen. K-nearest neighbors (KNN), naïve Bayes, and Logistics are also among the classifiers chosen. SFTA, local binary pattern (LBP), and Haralick feature vector are fed to the KNN, naïve Bayes, and logistics classifier. The outcomes of the experiment indicate that the SFTA texture feature surpassed all other texture features in all classifiers, making it the best texture feature to use in forgery detection. Haralick feature has the second-best texture feature performance in all of the classifiers. The performance using the LBP feature is lower than that of the other texture features. It also shows that the KNN classifier outperformed the other two in terms of accuracy. However, among the classifiers, the logistic classifier had the lowest accuracy. The proposed SFTA based KNN method is compared to other state-of-the-art techniques in terms of feature dimension and detection accuracy. The proposed method outperforms other current techniques.
Co-Authors Ali Maki Sagheer Chen Soong Der Ismail Taha Ahmed Norziana Jamil